# Deep Learning for Slum Mapping in Remote
Sensing Images: A Meta-analysis and Review

Anjali Raj, Adway Mitra
Centre of Excellence in Artificial Intelligence, Indian Institute of
Technology Kharagpur, West Bengal, India
Manjira Sinha
Tata Consultancy Services Research, Kolkata, India

###### Abstract

The major Sustainable Development Goals (SDG) 2030, set by the United Nations
Development Program (UNDP), include sustainable cities and communities, no
poverty, and reduced inequalities. Yet, millions of people live in slums or
informal settlements under poor living conditions in many major cities around
the world, especially in less developed countries. Emancipation of these
settlements and their inhabitants through government intervention requires
accurate data about slum location and extent. While ground survey data is the
most reliable, such surveys are costly and time-consuming. An alternative is
remotely sensed data obtained from very-high-resolution (VHR) imagery. With
the advancement of new technology, remote sensing based mapping of slums has
emerged as a prominent research area. The parallel rise of Artificial
Intelligence, especially Deep Learning has added a new dimension to this field
as it allows automated analysis of satellite imagery to identify complex
spatial patterns associated with slums. This article offers a detailed review
and meta-analysis of research on slum mapping using remote sensing imagery
(2014–2024), with a special focus on deep learning approaches. Our analysis
reveals a trend towards increasingly complex neural network architectures,
with advancements in data preprocessing and model training techniques
significantly enhancing slum identification accuracy. We have attempted to
identify key methodologies that are effective across diverse geographic
contexts. While acknowledging the transformative impact of Neural Networks,
especially Convolutional Neural Networks (CNNs), in slum detection, our review
underscores the absence of a universally optimal model, suggesting the need
for context-specific adaptations. We also identify prevailing challenges in
this field, such as data limitations and a lack of model explainability, and
suggest potential strategies for overcoming these.

###### Index Terms:

Slums, informal settlements, deep learning, satellite imagery, remote sensing,
review, meta-analysis

##  I Introduction

Slums, also known as informal settlements, are areas where people live in
deplorable and vulnerable conditions. They pose a significant challenge to
achieving equitable and sustainable development, particularly in the context
of SDG 11 [1], which aims to“make cities and human settlements inclusive,
safe, resilient, and sustainable.” The global population of urban slum
dwellers is estimated to be one billion, disproportionately affecting the most
impoverished nations [2, 3]. According to recent statistical data,
approximately 1.6 billion individuals out of the total urban population of
four billion are residing in slum areas [4, 5], and by 2050, this is expected
to exceed 3 billion [6]. These statistics highlight the urgent need to address
urban poverty and improve living conditions in informal settlements. Figure 1
shows the proportion of urban populations living in slums [7]. This provides
an overview of the global distribution of slums and helps identify regions
where urban poverty’s challenges are most severe.

Slum dwellers face numerous social, economic, and health challenges that
impact their quality of life. Socially, slums are characterized by high
population density, inadequate housing, and limited access to essential
services, leading to a lack of privacy and security for residents.
Economically, the informal nature of slum settlements results in limited
employment opportunities and financial instability. Health-wise, slums are
breeding grounds for communicable diseases due to overcrowding, poor
sanitation, and limited access to clean water and healthcare services [8].
Climate-induced migration makes these conditions worse, highlighting the need
for effective interventions [9, 10, 11]. Herein, slum detection plays a
crucial role in addressing these challenges by providing accurate and up-to-
date information about the location and extent of informal settlements.
Precise slum mapping enables policymakers and urban planners to allocate
resources effectively, prioritize infrastructure development, and deliver
essential services tailored to the needs of slum dwellers. By identifying
areas that require intervention, slum mapping facilitates targeted actions
that can significantly improve the living conditions in these communities
[12]. The insights gained from slum detection can integrate informal
settlements into the urban fabric, promote inclusivity and resilience in
cities, and contribute to the overall well-being of slum residents.

A significant obstacle to effectively managing slums and enhancing the living
conditions of their inhabitants is the lack of accurate information regarding
these areas. Slums evolve rapidly, while most countries conduct ground-based
censuses only on a decadal basis. In light of these challenges, remote sensing
emerges as a pivotal tool to shed light on the spatial characteristics and
dynamics of these settlements [13, 14]. The use of satellite images
facilitates the mapping of slums, providing critical data for infrastructure
development and urban planning. Integrating remote sensing data with different
algorithms and indicators [13, 14, 15] offers an understanding of the slum
communities. These technologies allow the analysis of urban topographies,
providing critical insights that support sustainable urban management and
planning. For instance, models applied to satellite imagery can detect subtle
changes in slum areas [16, 17, 18], facilitating timely interventions to
address issues of overcrowding and sanitation, thus also contributing to SDG 3
(Good Health and Well-being).

Accurate slum mapping enables targeted interventions that can improve the
living conditions in these communities, directly contributing to SDG 11. SDG
11.1 seeks to improve slums and provide secure, affordable housing and
critical services to all. Accurate slum mapping identifies regions that need
housing improvements and key services, enabling slum upgrading. SDG 11.3
encourages inclusive, sustainable urbanization and participatory, integrated,
and sustainable human settlement planning and management. In Nairobi, Kenya
[15], the Kibera slum upgrading project [19] used detailed mapping to redesign
the settlement, improving road networks, public facilities, and legal land
tenure. This initiative enhanced resident’s quality of life. It promoted
participatory urban planning, and by involving the community [20] in these
processes, it also supported SDG 11.3. The use of comprehensive slum maps in
Maharashtra, India, [21] has improved access to sanitation and clean water,
reducing disease outbreaks and also contributing to SDG 6 (Clean Water and
Sanitation). This effort has improved housing developments, making them safer
and more durable, and has also supported the inclusion of slum dwellers in
urban planning discussions. This shift towards data-driven, participatory
planning has improved living conditions and also fostered a sense of community
ownership and empowerment among residents, crucial for sustainable urban
development.

The IDEAMAPS Data Ecosystem [22] enhances slum mapping through artificial
intelligence and community engagement, supporting informed urban planning and
contributing to SDG 11. The SLUMAP project [23] uses remote sensing to map
slums in sub-Saharan Africa, influencing policies for SDG 11 and SDG 3. Such
technologies allow precise interventions to improve living conditions and
promote inclusive urban growth, making cities safer and sustainable. These
efforts highlight the importance of community involvement and data-driven
planning in transforming slums into integrated, resilient urban spaces. Hence,
the advancements in slum mapping technologies are not just academic; they
translate into tangible benefits that enhance the quality of life of millions
living in marginalized urban areas. Thus, slum mapping is critical to
achieving SDG 11 by providing the foundation for informed and inclusive urban
planning and policy-making.

Figure 1: Geographic distribution of the proportion of urban populations
residing in slums by country [7]. The color gradient indicates the percentage,
with darker shades representing higher proportions. The absence of colors
denotes the unavailability of data. Figure 2: Comprehensive Workflow of Deep
Learning for Slum Mapping. This diagram highlights the essential stages in the
deep learning process used for effective slum detection and analysis

As technology evolves, machine learning and deep learning have become crucial
to harness remotely sensed data [24, 25]. These algorithms can process
complex, high-dimensional imagery and extract intricate patterns often
invisible to human analysts, enhancing the precision and granularity of slum
detection. This study explores the relationship between remote sensing and
deep learning within the specific domain of slum mapping, critically
evaluating the deep learning architectures employed in this context. A
structured approach leveraging deep learning techniques is essential to
effectively tackle the challenges associated with mapping slums. Figure 2
illustrates the workflow adopted in this study. This workflow underscores the
methodical steps involved in deploying deep learning techniques for slum
mapping, setting the foundation for the detailed exploration of methodologies
discussed in the following sections.

The subsequent sections of this paper are structured as follows: Section II
provides an overview of several methodologies employed in slum mapping through
remote sensing imagery. Section III offers a concise overview of deep learning
architectures. Section IV presents a comprehensive meta-analysis of deep
learning-based algorithms used for slum mapping and identifies a few
challenges to focus on. Section V examines the issues mentioned in the
preceding section in detail and discusses the approaches taken in the
literature. Section VI explores integrating deep learning with Geographic
Information Systems (GIS) software. We discuss various ethical concerns and
limitations of remote sensing in Section VII and draw our conclusions in
Section VIII.

##  II Slum Mapping using Remote Sensing

###  II-A Technological Evolution in Slum Mapping: From Ground Surveys to
Remote Sensing

Slum mapping has evolved from labor-intensive census surveys [26, 27] and data
gathering to the advanced analysis of satellite imagery. Modern remote sensing
technologies, like the Sentinel, Ikonos, and WorldView satellites, have
changed this field by providing moderate to very high-resolution (VHR) imagery
(In this study, we define VHR images as those with a spatial resolution of 1 m
or less, high-resolution images with a spatial resolution between 1 m and 5 m,
and moderate-resolution images with a spatial resolution between 5 m and 10m.
A spatial resolution of over 10 m is regarded as a low-resolution image). This
has greatly improved urban data processing [17, 28, 29], made images more
detailed, and helped researchers figure out the complex urban features of slum
areas.

In addition to satellite imagery, aerial imaging techniques have seen
substantial progress. Unmanned Aerial Vehicles (UAVs), or drones, have become
increasingly popular for capturing high-resolution images of urban
environments [30]. Furthermore, the integration of advanced sensors on drones
and aircraft provides high-resolution data crucial for delineating intricate
slum features. The integration of LiDAR technology with remote sensing has
further enhanced the accuracy of slum mapping [31]. LiDAR sensors, mounted on
satellites, aircraft, or drones, use laser pulses to create detailed
representations of urban environments and capture the topography of slum
areas.

Before adopting machine learning approaches, object-based image analysis
(OBIA) and texture analysis were commonly employed to identify slum areas.
These methods, discussed in detail in the subsequent subsections, have laid
the groundwork for the current state-of-the-art approach to slum mapping. The
transition from conducting surveys on-site to using remote sensing techniques
represents a shift towards an efficient, scalable, and objective approach to
analyzing urban landscapes [32, 33]. This technological evolution has paved
the way for creating specific policies and interventions that cater to the
ever-evolving needs of metropolitan regions, contributing to effective urban
planning and policy-making.

###  II-B Texture Analysis in Slum Mapping

Texture analysis plays a vital role in remote sensing for slum mapping, as it
helps to distinguish informal settlements from formal urban areas based on
their unique spatial configurations and architectural patterns. In this
section, we emphasize the importance of texture analysis in slum mapping and
present studies where this approach has achieved successful results.

High-resolution satellite images reveal textural contrasts between slums and
formal urban areas, serving as a key identifier for remote sensing techniques.
There are two primary approaches for texture analysis: structural methods like
mathematical morphology (MM) [34, 35], and statistical methods like the Grey
Level Co-occurrence Matrix (GLCM) [36]. Structural approaches analyze images
by examining geometric structures, while statistical techniques focus on
pixel-intensity arrangements to identify disordered texture patterns.

Several studies have demonstrated the effectiveness of integrating spectral
information with texture analysis to improve classification accuracy. For
instance, [29] integrated spectral information with GLCM variance to
differentiate slums from formal regions. [37] found that local directional
pattern (LDP) techniques outperformed GLCM in categorizing informal
settlements due to LDP’s sensitivity to texture directional variance. [38]
employed a Random Forest (RF) classifier with GLCM and differential
morphological profiles (DMP) to detect slums, highlighting the power of
machine learning algorithms in analyzing complex texture-based data for
precise slum mapping. [39] compared statistical and spectral features and
found that spectral characteristics better described urban slum textures than
statistical descriptors. [40] found that scale and anisotropy affect texture
indices, emphasizing the need to select appropriate scales for slum textures.
[41] expanded upon previous research by incorporating statistical, spectral,
and structural techniques, including morphological profiles (MP) and
mathematical morphological profiles (MMP), to differentiate different textures
present in slum areas. They employed the minimum redundancy maximum relevance
(mRMR) feature selection method before using a support vector machine (SVM)
classifier. These studies demonstrate the importance of texture analysis in
slum mapping, as it enables the extraction of meaningful information from
high-resolution satellite images to distinguish slums from formal urban areas
accurately. The methodologies used in these studies, including GLCM, LDP, and
morphological profiles, highlight the diversity of approaches available for
texture analysis in remote sensing applications.

While texture analysis has improved slum detection, challenges persist. The
texture measures for slums can differ between locations, even within the same
slum. This can be explained by variations in slum characteristics, such as the
physical dimensions, layout, and materials used in construction [42, 43].
Thus, patterns in one image may not work for another image as image texture
does not analyze pixels individually. Instead, it focuses on groups of pixels
that create distinct patterns to distinguish features. However, satellite
imaging and machine intelligence offer exceptional opportunities to enhance
urban planning and resource allocation. Researchers and practitioners can
attain detailed and flexible comprehension of informal urban settlements by
combining deep learning techniques with texture analysis. Deep learning
models’ automatic feature learning and multiscale analysis can improve the
precision and effectiveness of slum mapping, leading to targeted and effective
interventions for urban development.

###  II-C Object-Based Image Analysis (OBIA)

OBIA considers an image to be a collection of objects and uses attributes such
as size, shape, texture, and relationships with neighboring objects to extract
meaningful information. OBIA, in slum mapping, segments satellite images into
meaningful objects or groups of pixels. These objects are then analyzed based
on their spatial, spectral, and textural properties.

One of the earliest studies employing OBIA on slums was by [44]. This study
used eCognition software [45] to differentiate informal settlements from other
land-use types based on their unique attributes in Ikonos images over Cape
Town. The eCognition software adheres to the principles of object-oriented
programming. Additionally, it has a patented methodology for image
segmentation that operates across many scales. However, the approach was
complex and tailored to a specific dataset, posing challenges in
generalizability. Subsequent studies have built upon [44], employing ontology-
based approaches and refining the classification of slums [46, 47, 48]. [49]
presented a thorough slum ontology known as the generic slum ontology (GSO).
This ontology organizes concepts into three levels: environment, settlement,
and object, providing a framework for classifying slums based on image data.
This ontology-based approach has been applied in other studies [50], based on
the vegetation, impervious surface, and bare soil (V-I-S) model [51]. The
advancements in OBIA have seen the integration of additional indicators and
image-based features to improve the accuracy of slum mapping[52]. [53]
incorporated indicators such as plant cover, built-up area, iron cover,
asphalt cover, and texture, along with NDVI, BAI, iron index, REI, coastal
blue index, and texture for classification. [54] used WorldView-3 imagery and
a Geographic Object-Based Image Analysis (GEOBIA) processing chain framework
to classify land cover in impoverished urban areas of Nairobi, Kenya,
demonstrating the effectiveness of OBIA in capturing land cover
characteristics in slum contexts. These studies demonstrate the diverse
applications and effectiveness of OBIA in accurately identifying and
characterizing informal settlements.

The progress in OBIA offers significant prospects for improving the accuracy
and granularity of slum mapping. Nevertheless, there are still obstacles when
applying findings to a wide range of situations and effectively handling
intricate information. An issue arises when vegetation and shadows obstruct
some parts or entire buildings, resulting in decreased accuracy [55, 56].
Another concern with OBIA is the high level of spectral noise caused by the
materials used in the construction of slum houses. Unpaved roads have spectral
reflectance similar to slum rooftops, making it difficult to differentiate
dwellings. Furthermore, the algorithms used to identify slums in an image are
context-specific, restricting their applicability in other geographical
regions [57]. By harnessing the capabilities of deep learning, OBIA can
provide more precise and comprehensive identification of slum areas, improving
urban planning and policymaking.

###  II-D Data Mining Techniques for Slum Identification

Data mining techniques have become increasingly popular in recent years for
identifying slums. These methodologies use various tools to uncover new
patterns in large datasets, with machine learning and deep learning being the
cornerstone of these strategies.

Several studies have applied data mining techniques to slum mapping. [58]
developed an approach using symbolic machine learning and association analysis
to extract image data from SPOT-5 satellite imagery in South Africa. [59]
employed logistic regression (LR), SVM, and RF algorithms to classify urban
areas as slums or non-slums using spectral, texture, and structural properties
extracted from Google Earth imagery. [60] used WorldView-2 images and
auxiliary spatial data to identify deprived regions in Mumbai, employing both
RF classifiers and LR models. [61] tested SVM and RF for slum mapping in
Bandung, Indonesia, using features extracted from a local slum ontology. [62]
used the Bag of Visual Words framework and Speeded-Up Robust Features (SURF)
for pixel-level classification of slums in Kalyan and Bangalore, India.[15]
employed discriminant analysis, LR, and See5 decision trees to assess the
spatial extent and changes of slums in three Kenyan sites.

Deep learning techniques have also been explored for slum mapping. [116] used
CNNs to identify informal settlements, comparing the CNN model against SVM
using GLCM and LBP features. [113] employed deep convolutional networks for
detecting small-scale deprivation zones in Bangalore using a U-Net-Compound
model for accurate city poverty mapping. [121] examined the relationship
between deprivation and image-based traits in different slums using CNN-
learned spatial features. In [132], transfer learning was used to train a
model on QuickBird images and apply it to Sentinel-2 and TerraSAR-X data for
slum mapping. Transfer learning has also been used in [131, 138]. [18]
suggests using fully convolutional networks (FCNs) with dilated convolutions
to analyze temporal trends in transient slum clusters in Bangalore. In [63], a
dilated kernel-based deep CNN (DK-DCNN) technique was used to detect urban
slums in Indian cities, using GLCM texture features and WFT-based spectral
features for classification.

Data mining has been used to identify informal settlements using UAV features
[30], SAR data [64, 65], and Lidar data [31], showcasing the versatility of
slum mapping in diverse urban landscapes. To improve urban settlement mapping,
[66] categorized land cover into six types using Landsat-8 and non-spectral
data (digital elevation models and road networks). [67] examined the rank-size
distributions of morphological slums in different cities to determine if they
are comparable. They found that typical patterns found between cities can be
applied to patterns within cities. [68] found morphological slums in eight
cities in Africa, South America, and Asia and looked at how their sizes were
spread based on [67]. [69] examined slum identification and delineation
deviations in VHR images from Ahmedabad (India), Nairobi (Kenya), and Cape
Town (South Africa). Existential and extensional uncertainty in slums were
shown with random sets, and bootstrapping was used to find confidence in the
different definitions. They also found the built-environment criteria that
experts use to spot slums in areas that do not normally have slums. The
IMMerSe (integrated methodology for mapping and classifying precarious
settlements) methodology was used to identify precarious settlements in the
Baixada Santista metropolitan region of So Paulo [70]. It characterized the
urban environment using high-spatial-resolution images without digital image
processing by extracting density and urban organization level. [71] proposed
the systematic semi-automated SLUMAP framework based on free, open-source
software that gives policymakers information about poor urban areas in Sub-
Saharan Africa. The application of RF on Landsat 8 has been tested in [72]
using two approaches for developing the training set: using available
databases (maps for informal settlements) and visual interpretation using VHR.
OpenStreetMap (OSM) was used in the secondary approach to build the sample set
for the classes with the lowest accuracy and precision in the first round of
classification. According to [73], a realistic-dynamic urban modeling system
can detect informality, slums, and pedestrian and transit modes in urban
landscapes using aerial and street view images. The model uses two deep CNNs
pre-trained differently on various data sets to extract and geo-reference
information from unlabeled urban scene images from around the world. The use
of different data sources, from satellite imagery to Lidar data, highlights
the adaptability of these methodologies.

Data mining techniques are increasingly integrated with other technologies,
such as OBIA and GIS, to enhance slum mapping [74, 75, 76]. This integration
uses each approach’s strengths to analyze informal settlements more thoroughly
and accurately. For instance, using OBIA and machine learning enables the
classification of complex urban structures by segmenting high-resolution
images into meaningful objects and then applying data mining algorithms to
these objects. [77] employed this approach to define slums in Jeddah. This
integration allows for a more detailed understanding of slum areas,
facilitating the development of targeted interventions. Similarly, GIS
technologies are combined with data mining techniques to incorporate spatial
analysis into slum mapping. GIS provides a framework for managing and
analyzing spatial data, while data mining techniques can uncover patterns and
relationships within this data. By integrating these two approaches,
researchers can create more sophisticated models that consider the spatial
distribution of slums and their relationship to other urban features.

Machine learning and deep learning techniques have become increasingly popular
in slum mapping due to their ability to handle complex data and extract
meaningful patterns. These techniques can be applied in various ways.

  * •

Feature Extraction: Machine learning algorithms can automatically identify
relevant features from satellite imagery that distinguish slums from other
urban areas. Features such as texture, shape, and spectral signatures are
commonly used.

  * •

Classification: Once features are extracted, machine learning classifiers,
such as RF, SVMs, and LR, can classify areas as slums or non-slums.

  * •

Deep Learning Architectures: Deep learning models, particularly CNNs, can
automatically learn hierarchical feature representations from raw imagery
data. This is useful for capturing the complex spatial patterns of slums.

  * •

Transfer Learning: In situations where labeled data is scarce, transfer
learning can be applied to adapt pre-trained deep learning models to slum
mapping, leveraging knowledge from other domains.

The implications of these findings for future research and policy-making are
significant. Accurate slum maps generated through these advanced techniques
can inform targeted interventions for slum upgrading, infrastructure
development, and service provision, contributing to the sustainable
development of urban areas.

###  II-E Recommendations and Comparative Analysis for Slum Mapping Approaches

Based on various remote sensing techniques for slum mapping, we provide the
following practical guidance and comparative analysis to assist researchers
and practitioners in selecting the most suitable approach for their projects:

  * •

Utilize advanced satellite systems like WorldView for large-scale projects
requiring very high spatial resolution. For more detailed imagery, consider
aerial imaging techniques with drones.

  * •

Employ texture analysis methods, such as GLCM or mathematical morphology, to
capture the distinct architectural patterns of slums, especially in complex
urban environments.

  * •

Use OBIA for detailed feature analysis and to reduce classification noise.
This approach is particularly useful for projects that require a nuanced
understanding of slum features.

  * •

Automate feature extraction and classification with machine learning and deep
learning techniques. These approaches help handle large datasets and capture
complex spatial patterns.

##  III Unveiling Architectures: A Dive into Convolutional Neural Networks and
Autoencoders

Deep learning [78] has been recognized as one of the ten groundbreaking
technologies of 2013 [79]. It involves neural networks (NN) with multiple
hidden layers, distinguishing it from shallower learning models. The training
procedure for the deep learning model involves forward propagation, where the
model predicts outputs from inputs, and backpropagation, where errors are
minimized by adjusting weights and biases. Top companies like Google,
Microsoft, and Facebook have invested in deep learning for applications such
as image segmentation and object detection, demonstrating superior performance
in complex computational tasks. Due to its remarkable achievements, it has
become increasingly popular as the preferred model in numerous application
domains. Following this achievement and the enhanced accessibility of data and
computational resources, it is gaining momentum even in remote sensing.

Deep learning has enhanced the capabilities of remote sensing and urban
analysis through the processing and analysis of complex data. Remote sensing
uses deep learning algorithms for tasks such as land cover classification [80,
81, 82], object detection [83, 84, 85], and change detection [86, 87, 88].
These techniques excel at evaluating high-resolution satellite and aerial
images, allowing for precise and detailed urban mapping. Within urban
analysis, it allows for the automated extraction of complex patterns present
in densely populated urban areas, helping in detecting slums. This section
provides a concise overview of deep-learning architectures employed in slum
mapping.

###  III-A Convolutional neural networks (CNNs)

Figure 3: Architecture of a Convolutional Neural Network (CNN)

Yann LeCun introduced convolutional networks in 1989 [89], which fundamentally
consist of three stages: convolution to generate linear activations,
application of nonlinear activation functions like ReLU [90], and pooling [91]
to reduce dimensionality. These layers, along with fully connected layers,
help CNNs capture complex hierarchical image representations. Figure 3 shows
the basic architecture of a CNN. Notable for their versatility and
scalability, CNNs have excelled in various computer vision tasks. For
instance, AlexNet [92] won the 2012 ImageNet challenge, demonstrating the
power of deep CNNs. VGG networks [93] improved classification by deepening
network structures, while ResNet [94] introduced skip connections to ease
training deep networks. Fully Convolutional Networks (FCNs) [95] use an
encoder-decoder structure to handle various input sizes, crucial for tasks
like semantic segmentation.

###  III-B Autoencoders

Figure 4: Encoder-Decoder structure of an Autoencoder.

An autoencoder is a NN designed to replicate its input data at its output. It
consists of an encoder function $h=f(x)$ that compresses the input into a
lower-dimensional latent space and a decoder function $r=g(h)$ that
reconstructs the input from this compressed representation. Figure 4 shows the
basic encoder-decoder structure of an autoencoder. They have been particularly
effective in remote sensing for feature representation, as noted in [96, 97,
98].

##  IV Meta-Analysis

The use of meta-analysis has emerged as a fundamental methodology in numerous
academic fields. It allows researchers to combine data and conclusions from
several studies to gain broader insights that may not be readily evident from
any single study. The word “meta-analysis” was first used in academia to
organize and consolidate the examination and amalgamation of numerous studies.
[99] defines it as a systematic procedure for reviewing and combining various
analyses. Over time, meta-analysis has undergone a process of evolution and
refinement. In essence, it is not simply a compilation of several studies but
rather a rigorous methodology that integrates findings from comparable
studies, as detailed by [100]. The selection and evaluation of pertinent
studies are fundamental components in the execution of a meta-analysis.
Researchers often use established procedures to select studies uniformly,
clearly, and thoroughly. One example of a generally acknowledged standard is
the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) [101]. PRISMA, a framework developed to assist researchers in
conducting systematic reviews [102] and meta-analyses, places significant
emphasis on the importance of clear and comprehensive reporting. The use of
the PRISMA protocol is not limited to any discipline. For example, research
conducted by [103] and [104] has demonstrated the PRISMA protocol’s
applicability in urban geography. Different perspectives can be gained by
using meta-analysis in scholarly research. This helps researchers find
recurring patterns, come up with stronger conclusions, and propose
recommendations based on a wider range of data. In the context of slum mapping
using deep learning, meta-analysis is particularly relevant due to the diverse
methodologies, varying data sources, and the need for consolidated insights to
guide future research and applications.

###  IV-A Selection of scientific articles

In this information age, the methodology employed to sift through the vast
literature is of the utmost significance. The objective of this study is to
compile and evaluate studies that use deep learning in the context of mapping
slums. This section provides an account of the approach employed for selecting
and reviewing the papers. The study used scholarly articles obtained from
three widely recognized academic databases, namely Web of Science, Scopus, and
Science Direct. These databases are well-known for their extensive compilation
of high-quality research articles spanning several academic fields. The
publications chosen for this study were limited during the timeframe of
2014–2024. This selection was made to provide a thorough understanding of the
latest developments in the field. A precise search query was created to ensure
the articles were pertinent to the study’s subject. The search focused on the
database record title, abstract, and keywords sections. The search query
performed consisted of the terms: “slum” OR “informal settlement” AND “remote
sensing” OR “satellite imagery” AND “deep learning” OR “neural networks”. The
search was done on April 5, 2024. After performing the search query, the
database yielded a cumulative count of 207 publications. However, not all
these papers were relevant for our review. Consequently, further filtering
procedures were implemented; review articles were excluded. The scope of this
study was limited to papers authored exclusively in English. An examination of
article duplication was conducted, and redundant articles were removed.
Exclusions were made for grey literature, book sections or chapters, reports,
and thesis publications. Also, studies that did not pertain directly to using
deep learning techniques in examining slums were excluded. Studies without
sufficient methodological details or validation processes were also excluded.
Only studies that provided full-text access were included. Any articles
inaccessible due to a paywall or lack of institutional access were removed
from the analysis. Studies on change detection, land use, land cover, health,
sanitation facilities, and settlement expansion were omitted. After filtering,
40 publications [105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144] were retained
and included in the final evaluation, highlighting the growing yet still
nascent exploration of this field. This approach ensures that the findings are
robust, laying a solid foundation for future inquiries into deep learning
applications in mapping slums. The PRISMA flow diagram was used to visually
depict the process of paper selection. The breakdown of the items included and
excluded from the selection process is provided in Figure 5. Our findings
indicate a substantial potential for growth in the application of deep
learning to slum mapping. This meta-analysis underscores the diversity in
regional studies and data sources and the evolution of deep learning
approaches in this domain. These insights can help improve urban planning and
policy-making, directing resources more effectively to improve living
conditions in slum areas.

Figure 5: PRISMA flow diagram depicting the systematic selection process of
the articles.

Understanding the distribution of articles throughout different journals
offers valuable information about the predominant literature sources within
this field. Table I illustrates the number of articles selected from different
publications. The methodology in this study guarantees the reliability and
accuracy of the findings and insights obtained from the review. These findings
represent the current advancements in the application of deep learning
techniques in mapping slum areas.

TABLE I: Distribution of the selected articles across different journals Journal | Publications
---|---
Sustainable Cities and Society | 1
Photogrammetric Engineering and Remote
Sensing | 1
Sustainability (Switzerland) | 1
Habitat International | 1
International Journal of Electrical and Computer Engineering | 1
ISPRS Journal of Photogrammetry and Remote Sensing | 1
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 1
CEUR Workshop Proceedings | 1
IEEE International Conference on Data Science and Advanced Analytics | 1
ISPRS International Journal of Geo-Information | 1
Sensors (Switzerland) | 1
Cities | 1
Neural Computing and Applications | 1
Landscape and Urban Planning | 1
International Archives of the Photogrammetry, Remote Sensing and Spatial
Information
Sciences | 1
npj Urban Sustainability | 1
Remote Sensing Applications: Society and
Environment | 1
International Journal of Applied Earth
Observation and Geoinformation | 2
International Geoscience and Remote Sensing Symposium (IGARSS) | 2
IEEE Transactions on Geoscience and Remote Sensing | 2
Remote Sensing of Environment | 2
IEEE Geoscience and Remote Sensing Letters | 2
Scientific African | 2
Joint Urban Remote Sensing Event, JURSE | 3
Computers, Environment and Urban Systems | 4
Remote Sensing | 4

###  IV-B Points of Focus

Figure 5 displays the total number of papers chosen for this meta-analysis
study. This study aims to examine deep learning techniques in these papers in
the context of slum mapping. Our analysis aims to address the following
questions:

  1. 1.

What are the regions of study? \- We aim to identify the regions where these
papers have trained and tested their models. This is important because slums
may have very different characteristics in different parts of the world.

  2. 2.

What are the data sources and the datasets used? \- Since there are no well-
known, annotated, and standardized datasets available for the task of slum
mapping, the choice of the data source is an important decision for any
comprehensive investigation of this task.

  3. 3.

How are the training and testing data prepared for the model? \- In addition
to the dataset itself, it is important to understand the pre-processing, data
augmentations, imbalance corrections, and any other methodologies employed to
adequately prepare the data for modeling.

  4. 4.

What is the structure of the predictive model? \- This delves into the
technical details of the modeling process, including the architectures
employed, loss functions, optimizers, and hyper-parameters.

  5. 5.

What are the different metrics involved in performance evaluation? \- The
evaluation metrics play a pivotal role in understanding the efficacy and
dependability of the models. Furthermore, the choice of metrics is vital to
answering the specific questions being addressed by a work.

##  V Analyzing the Points of Focus

Now, we take a detailed look at each of the points of focus identified above.

###  V-A Study Regions

Figure 6: Continent-wise distribution of study regions

Here, we examine the geographical distribution of study regions that have been
focused on the detection of slums using deep learning techniques. We conducted
an analysis at both the continental and country-specific scales. Based on the
analysis shown in Figure 6, it is apparent that Asia, followed by Africa, was
the primary region of interest for such studies. As demonstrated in Figure 7,
India leads the chart with 29% of the research, followed by China and Kenya.
The investigations on slums in India mostly focus on regions like Mumbai and
Bangalore [113, 118, 121, 123, 131, 132, 134, 138, 139]. Shenzhen ([107, 111])
has emerged as a significant center of attention in China. Numerous African
nations [105, 116, 122], namely Kenya, Morocco, and Tanzania, have also been
the focus of these studies, with substantial research in Nairobi.

Figure 7: Countries from where study regions are frequently selected

###  V-B Data Sources

The Deep Learning model requires high-quality annotated data for training and
evaluation. Figure 8 illustrates the distribution of data sources used in deep
learning-based slum mapping studies. Sentinel satellites are the most commonly
used data sources, accounting 22% of the studies, followed by Google Earth
imagery at 20%. Pleiades and Quickbird comprise 12%, while WorldView 2 & 3
account for 10%. SPOT 5, 6, & 7 and Planet Labs represent 8% of the data
sources. Other sources, such as PlanetScope, GeoEye, and TerraSAR-X, comprise
the remaining 8%.

This variety of data sources reflects the range of resolutions and spectral
capabilities that researchers require for different aspects of slum mapping.
The detailed imagery from providers like DigitalGlobe (e.g., Quickbird,
WorldView) is crucial for fine-grained analysis and feature extraction within
slum areas. QuickBird was a VHR commercial EO satellite launched in 2001 and
decommissioned in 2015. It provided imagery at 60 cm in panchromatic (PAN)
mode and 2.4 m in multispectral (MS) mode, capturing data in the blue, green,
red, and near-infrared spectrum. QuickBird’s capabilities made it one of the
leading sources for urban planning, environmental monitoring, and mapping
services, offering detailed views that can be used for slum mapping and land-
use studies [132, 141]. The WorldView series are commercial EO satellites
operated by Maxar Technologies. It provides PAN imagery at 0.46 m and eight-
band MS imagery at 1.84 m, using four commonly used colors (red, green, blue,
and near-infrared 1) and four spectral bands (coastal, yellow, red edge, and
near-infrared 2). WorldView-3’s 31 cm PAN resolution and MS bands—short-wave
infrared (SWIR) and CAVIS (Cloud, Aerosol, Vapour, Ice, Snow)—help identify
materials and terrain types. This makes WorldView satellites suitable for
detailed mapping, including urban analysis and slum detection [108, 122, 123].
On the other hand, Sentinel satellites are advantageous for large-scale,
regional studies. The Sentinel satellites are part of the Copernicus
Programme, coordinated by the European Space Agency (ESA), providing a
comprehensive EO system. Sentinel-1 provides radar imagery, ideal for
monitoring urban expansion, land surface for motion risks, and infrastructure
stability. Sentinel-2’s 10 m resolution allows for comprehensive images of
large areas, which is vital for monitoring land changes and vegetation. It is
also extensively used for slum mapping [126, 135, 139] as it is openly
available. Google Earth provides accessible data that is frequently updated,
which can be beneficial for time-series analyses and monitoring changes over
time.

The usage of such diverse data sources underscores the adaptability of deep
learning methods to handle different spatial resolutions and spectral
characteristics critical for accurate slum detection and mapping. This
distribution suggests that a combination of different data sources can offer a
comprehensive understanding of slums. High-quality and varied data sources are
essential for developing robust deep-learning models to achieve effective
urban management and policy-making.

Figure 8: Dominant Satellite Platforms

###  V-C Data Preparation

Data preparation for deep learning applications in slum detection is a multi-
step procedure designed to handle the complexities of urban informal
settlements, as seen in satellite imagery. In this section, we present the
methods employed in pre-processing, preparation, and augmentation, with a
focus on their methodological rationale and contextual significance in slum
mapping.

  * •

Pre-processing: It plays a crucial role in preparing remote sensing imagery
for feature extraction and model training. This involves adjusting image
attributes at the pixel or spectral level to better capture the physical
characteristics of slum environments. For instance, spectral descriptors [106,
107] are created from high-resolution imagery to capture the unique material
signatures within slum environments. GLCM measures delineate the textural
characteristics of slums, and structural patterns are retrieved to distinguish
land features. Image enhancement and atmospheric correction are used to obtain
surface reflectance values needed to train models to accurately interpret
real-world conditions [142, 145]. Pan-sharpening methods are applied to MS
bands to increase the clarity and detail of the imagery [115], an essential
step for resolving the fine-grained spatial structures prevalent in slums.
Specialized software tools such as ArcGIS, which facilitates complex spatial
analysis, and ENVI, which aids in the processing of geospatial data, are
essential for enhancing and refining these images [124, 145].

  * •

Data Cleaning and Integration: Data cleaning involves de-skewing images to
correct any tilt and cropping to focus on the areas of interest, removing
irrelevant information that could confound the learning process [120].
Integrating datasets from diverse sources is a pivotal task as it allows for
the consolidation of data, providing models with a comprehensive view of the
urban landscape. Furthermore, binary masking delineates areas, while
normalization standardizes data values, ensuring dataset consistency and
comparability.

  * •

Data Augmentation: It plays a crucial role in augmenting the diversity and
quantity of training data, thereby enhancing the accuracy and generalization
capabilities of the model. In the context of slum mapping, where satellite
imagery exhibits varied and complex urban textures, augmentation helps models
adapt to the different visual characteristics of slums. Several key data
augmentation techniques include random cropping and image tiling, which are
used to focus on different parts of an image, ensuring the model can recognize
slums from partial views. This is particularly useful when working with large
areas, allowing the model to focus on detailed segments of the imagery,
typically in sizes of 256x256 or 512x512 pixels. Flipping and rotation are
also extensively used. Brightness adjustments and noise addition further
enhance the feature set, ensuring that the model is robust. These
transformations allow the model to learn generalized features that are
resilient to changes in orientation, scale, and translation, a critical aspect
for slum identification in heterogeneous urban landscapes [108, 122, 142].
Affine transformations, which include minimal shearing and stretching, help
the model generalize across different sensor angles and perspectives,
mimicking real-world variations. To ensure effective learning, datasets are
typically divided into training and testing subsets, often using an 80-20 or
70-30 split. Independent shuffling of the datasets is crucial to avoid biases
related to the data, ensuring the model learns to generalize from a
representative sample of urban landscapes.

  * •

Ground Truth Label Generation: Ground truth labels and masks are essential in
these studies, as they act as reference data for training and evaluating
models. Several studies used manual annotation of satellite images [105, 111,
122, 135]. This method entails the involvement of human experts who identify
and demarcate urban villages, slum regions, and areas of deprivation. This
process of manual annotation ensures accurate and reliable ground truth
labels. Multiple studies integrated a variety of data sources, including
remote sensing data, street view images, and social sensing data [107, 124,
129]. The ground truth labels were created by combining these various data
types, allowing for more thorough and precise model training. This label-
generating process is enriched with expert knowledge as it offers valuable
perspectives on distinct characteristics in urban regions, aiding in the
creation of reliable labels.

TABLE II: Data Preparation Techniques for Deep Learning in Slum Identification. Category |  Techniques
---|---
Pre-processing Techniques |  Spectral descriptors extraction, GLCM metrics computation, structural patterns extraction, image enhancement, atmospheric adjustments, pan-sharpening of multispectral bands
Data Cleaning and Integration |  De-skewing of images, cropping to focus areas of interest, data merging from various sources, normalization of data values
Data Augmentation Techniques |  Random cropping, flipping and rotation, brightness enhancements, shearing and stretching
Dataset Preparation |  Segmentation into training and testing, image tiling, and independent shuffling of datasets
Ground Truth Label Generation |  Manual annotation, integration of diverse data for label generation with expert involvement

The selected studies demonstrate a wide array of data preparation techniques
(see table II), indicative of the complex nature of slum environments in
satellite imagery. The data preparation stage sets the stage for effective
model training, essential to accurately map slums. Through meticulous pre-
processing, cleaning, augmentation, and label generation, the groundwork for
deep learning models to learn from and adapt to the complexity inherent in
slums is established. The findings show that models must be trained on high-
quality, representative datasets with extensive data preparation.
Practitioners should focus on robust pre-processing to improve model accuracy.
For policymakers, this underlines the importance of data collection and
preparation standards, ensuring that efforts in slum mapping yield useful and
reliable information for decision-making.

###  V-D Modeling Approaches

The training process plays a crucial role in implementing deep learning models
for slum detection. Here, we outline the approaches used in training,
encompassing network designs, frameworks, optimizers, and loss functions,
among other factors.

####  V-D1 Network Topologies

The performance of deep learning models in slum detection depends on choosing
suitable network topologies based on the datasets described in the previous
section. These models must not only accurately represent the complexities of
urban landscapes but also be able to adjust to the inherent variability found
in slum areas. A diverse range of architectural models has been developed,
including semantic segmentation using variations of the U-Net [105, 106, 113,
126, 142]. These models use deep learning techniques to accurately identify
and outline the complex and irregular patterns found in slums. Additionally,
hybrid models like GASlumNet [106] combine the strengths of well-established
networks like ConvNeXt to enhance performance. Another method involves using
pre-trained models that leverage the knowledge gained from other large image
datasets. This allows for the necessary adjustments to be made in order to
accurately map slum areas. The refinement of such models is enhanced through
unique architectures and multimodal networks [119, 143], highlighting the
intricate nature of slum detection. This is evident in networks that
incorporate transformer-based fusion approaches [119, 143], which indicates a
move towards using a variety of data sources. These deep learning models are
carefully curated to turn raw data into practical insights about network
topology.

####  V-D2 Loss Functions and Optimization Algorithms

Figure 9: Distribution of Optimizers Employed in Slum Detection Studies, with
Adam emerging as the predominant choice

Deep learning for slum identification has used a variety of loss functions and
optimization techniques to address the challenges posed by the complexity of
urban informality. The Adam optimizer, as shown in Figure 9, has been a
popular choice because of its ability to adjust the learning rate based on the
significant variability found in slums. This optimizer, typically initialized
with learning rates of 0.001, has played a pivotal role in various research,
enabling consistent convergence and dependable performance.

The commonly used loss function in conjunction with Adam optimization is the
weighted cross-entropy (WCE) loss. This loss function addresses class
imbalance by allocating varying weights to classes according to their
frequency. It is particularly important in slum identification, as the slum
areas may only make up a small part of the cityscape yet are of great
importance. Simultaneously, the Dice loss function is frequently used to
segment slum regions, boosting model sensitivity and specificity by aligning
predicted and ground-truth segments. A study also suggests combining Dice loss
with WCE to make a hybrid loss function [106] to effectively deal with the
intricacies of slum detection. Dice loss, which emphasizes spatial overlap,
and WCE, which handles imbalanced datasets, create a comprehensive learning
process.

The use of Adam and hybrid loss functions is the prevailing trend in deep
learning methodologies for slum detection. However, the optimization algorithm
and loss functions can be meticulously adjusted to tackle the unique
requirements of the task. These methods train deep learning models to not only
distinguish between slum and non-slum areas but also to enhance their
understanding of the varying levels of informality within the urban landscape.
This is crucial for accurate mapping and subsequent interventions.

####  V-D3 Deep Learning Framework, Hyperparameters, and Regularization

Deep learning frameworks, hyperparameter optimization, and regularisation
techniques are crucial for improving the model performance for slum
identification. TensorFlow and Keras are the dominant frameworks in this field
(see Figure 10). Their user-friendly interface, extensive documentation, and
strong community support make them ideal for academic and practical research.
PyTorch, TensorFlow, Keras, and Theano are known for their adaptability and
comprehensive libraries, enabling various network designs and training
methods. PyTorch’s adaptability has been advantageous for developing
topologies like U-Net in conjunction with ConvNeXt [106], while Keras and
TensorFlow are popular for their user-friendly APIs and simplicity in applying
transfer learning [130, 138]. These frameworks enable the creation of various
deep learning models with intricate topologies and offer smooth support for
computing, which is essential for handling large remote sensing datasets.

Figure 10: Preference of Deep Learning Frameworks in Slum Detection Research,
showcasing TensorFlow’s dominance

Hyperparameter optimization is crucial for model performance as it directly
impacts the model’s convergence rate and overall performance. Learning rates
commonly commence at $10^{-3}$ or lower, whereas weight decay parameters are
typically within the range of $10^{-4}$ to $10^{-5}$ to guarantee robust
learning without encountering overfitting. Several techniques, such as the
Cosine learning rate decay and warm-up procedures [111], are used to optimize
the learning process dynamically. This meticulous calibration enables models
to successfully navigate the complex features of slums.

Regularization techniques are commonly employed to mitigate overfitting, a
prevalent issue when training models on high-dimensional data. L2
regularization and Dropout ensure that every neuron contributes to learning by
adapting to the model’s structure and training patterns. The use of Xavier and
He initialization, as well as BatchNorm and Dropout, are used to make the
model better at applying what it has learned from training data to situations
it has never seen before in the real world.

These strategies improve model training using initialization and
regularization techniques, optimized learning rates, and powerful deep-
learning frameworks. The meticulous handling of hyperparameters and
regularisation at a granular level highlights the need for a nuanced strategy
to tackle the intricacies of slums in satellite imagery. With advanced
training methods, deep learning models have improved their ability to identify
and categorize slum regions.

###  V-E Metrics Used

Deep learning for slum detection involves evaluating models across a range of
metrics that capture the characteristics of urban informality. These metrics
interlace diverse architectural choices and preprocessing strategies discussed
in previous sections to determine model applicability. The common metrics of
accuracy, precision, recall, and the F1 score form the cornerstone of model
evaluation. The true positive rate (TPR) and false positive rate (FPR) for ROC
curve analysis are additions to these, providing insights into the trade-offs
between sensitivity and specificity. Meanwhile, the Jaccard Index serves as an
indispensable metric, especially pertinent for segmentation tasks, gauging the
precision of overlap between the predicted slum areas and ground truth.

The overall accuracy (OA) shows how well the model fits the data, while the
Intersection over Union (IoU) or Jaccard Index looks at how well the segments
overlap, which is important given the slums’ spatial interweaving. Precision
and recall, along with the harmonizing F1-score, dissect the models’ ability
to assess urban complexity. The Kappa coefficient tempers these measures,
adding a layer of statistical robustness to the analysis. In class-wise
accuracy, the spotlight shifts to models’ capabilities in differentiating slum
typologies. The producer’s and user’s accuracy lend insight into the models’
reliability. The Area Under the Receiver Operating Characteristic (AUROC)
shows how well a binary classification works, while the Area Under the
Precision-Recall Curve (AUPRC) addresses the class imbalance in urban
datasets. Visual inspections add a qualitative dimension to this quantitative
array, ensuring that models hold up to statistical scrutiny and the human
eye—a crucial check for models for policy and urban planning.

The evaluation metrics employed in the reviewed studies reveal a multi-faceted
approach to model assessment. OA, IoU, precision (P), recall (R), and F1-score
(F1) are among the most commonly used metrics, providing a comprehensive view
of model performance across different dimensions. The emphasis on these
metrics confirms their importance in validating the reliability of deep
learning models in slum mapping. Multiple metrics allow researchers to assess
the accuracy, applicability, and fairness of the models across various urban
contexts. The precision generally falls within 87% to 95%, demonstrating the
models’ capacity to detect urban characteristics. Meanwhile, recall rates
typically show significantly lower values, indicating the difficulty of
accurately recording all relevant instances. The accuracy of the models
reaches high values, typically above 90%, which highlights their overall
usefulness. F1-score, Jaccard Index, Kappa (K), and mean intersection over
union (mIoU) frequently surpass 0.8, reflecting a robust balance between
precision and recall. This comprehensive evaluation suggests that deep
learning and remote sensing data yield reliable results in slum mapping for
urban planning and development.

By engaging with this elaborate suite of metrics, researchers ensure that
their models are theoretically sound and practically viable. For
practitioners, these metrics underscore the importance of rigorous validation
processes, and for policymakers, they spotlight the necessity of supporting
initiatives that emphasize a detailed approach to model assessment. This
comprehensive metrics analysis underscores the transition from precision
engineering in model development to precision efficacy in real-world
applications, enabling data-driven urban planning and policy-making.

##  VI Deep Learning Solutions in GIS Software

Deep learning applications in remote sensing have enhanced the analytical
capabilities of Geographic Information System (GIS) software, simultaneously
improving accessibility and user-friendliness. This advancement is
characterized by the incorporation of image classification tools coupled with
user-friendly graphical user interfaces (GUIs), making these computational
tools more accessible to individuals with rudimentary programming knowledge.

Table III provides an overview of the deep learning functionalities available
in commercial and open-source GIS software. This underscores the industry’s
commitment to promoting robust, user-friendly deep learning tools that
facilitate the broader adoption of CNNs among a diverse user base. Open-source
tools like QGIS and Orfeo ToolBox offer flexibility and community support,
appealing to users who favor transparency and customization. However,
proprietary software such as ERDAS Imagine and ArcGIS Pro provide integrated,
out-of-the-box solutions with enterprise-level support, which might be
preferable for users seeking a more guided experience.

TABLE III: Overview of deep learning functionalities in prevalent GIS software. Software |  Deep Learning Functionality | Licensing Model
---|---|---
QGIS |  Supports ONNX-based inference for tasks like segmentation and detection through the Deepness module. | Open-source
Orfeo ToolBox |  Features the OTBTF module, providing a deep learning framework specifically designed for processing satellite imagery. | Open-source
ERDAS Imagine |  Offers tools for image classification and feature extraction, including capabilities for semantic segmentation and object recognition. | Proprietary
eCognition |  Facilitates the creation, training, and deployment of CNNs utilizing the TensorFlow platform, tailored for intricate image analysis tasks. | Proprietary
ENVI |  Includes a specialized module for training deep learning models using TensorFlow, enhancing its utility in satellite and aerial image analysis. | Proprietary
ArcGIS Pro |  Supports deep learning analyses for both images and point clouds, incorporating architectures such as SSD and U-Net. | Proprietary
GRASS GIS |  Integrates with Python libraries like TensorFlow or PyTorch for spatial data analysis, offering capabilities for complex spatial modeling. | Open-source
PostgreSQL with PostGIS |  Facilitates the use of deep learning via extensions like PL/Python, allowing for sophisticated spatial queries and data analysis within a database framework. | Open-source

In addition to these platforms, the FOSS4G (Free and Open Source Software for
Geospatial) ecosystem democratizes advanced geospatial processing tools. It
provides several deep learning tools and modules, including Python-integrated
spatial analysis processing packages. These open-source technologies encourage
innovation and collaboration in the geospatial community.

As the geospatial field looks to the future, the integration of deep learning
tools in urban planning and policy-making frameworks presents opportunities
and challenges. These tools offer the potential to drive significant
advancements in urban management, but they must be tailored to varied urban
settings. These advanced analytical tools can lead to more informed decision-
making and contribute to urban sustainability. However, the transformative
power of deep learning in GIS must be managed with a sense of responsibility,
ensuring that its application is beneficial, ethical, and conducive to the
public good.

##  VII Discussion

The geographical scope and methodological breadth of the reviewed research
show that the integration of deep learning in the study of urban slums has
increased in recent years. This paper has dissected the layers of this
integration, presenting insights into regional focuses, data diversity, model
architectures, and the complex socio-economic tapestries these slums present.

###  VII-A Regional Focus and Implications

A significant concentration of research has been on slums in Asia and Africa.
The regions of Mumbai, Shenzhen, and Nairobi have each emerged as focal points
in the application of deep learning for urban slum study, highlighting the
global concern and interest in improving living conditions through enhanced
urban planning and policy-making. Each of these regions represents unique
facets of the global slum phenomenon.

  * •

Mumbai epitomizes the contrasts of thriving economic growth alongside
sprawling slums. It is home to some of the world’s largest informal
settlements, like Dharavi. Researchers have targeted Mumbai due to its
significant slum population, which presents many challenges, from congestion
to limited access to basic services, among others. The diverse texture of
Mumbai’s slums and their density make it an ideal case study for deploying and
testing deep learning models. To effectively map dense and diverse slum
regions, models must combine spectral and textural information, as
demonstrated by the remarkable performance metrics of GASlumNet [106]. Studies
employing modified CNN models, like VGG16-UNet and MobileNetV2-UNet, indicate
a focused effort to identify not just the slum areas but also essential
features within them, such as green cover and open spaces [118]. These studies
achieve high precision and recall, signifying the models’ accuracy in
classifying fine-grained details. Using pre-trained networks like VGGNet,
ResNet, and DenseNet in GeoInFuse [120] shows that existing architectures are
being improved by combining multiple channels of data to find different types
of urban forms. This could have profound implications for distinguishing
between formal and informal urban areas, which is crucial for effective urban
planning.

  * •

Shenzhen, one of China’s most populous cities, has transformed into a megacity
and hub of technology and innovation. Rapid urbanization and the migration of
millions into the city have led to the growth of informal settlements, often
overshadowed by the city’s global economic status. Studies such as the
hierarchical recognition framework for urban villages [107] combine remote and
social sensing data to address the challenge of detecting urban sprawls.
Integration of street-level imagery in the Vision-LSTM module [111] enhances
human-centric analysis of urban sprawl. The application of a transformer-based
multimodal fusion network (UisNet) [119] and a multilevel spatial-channel
feature fusion network (FusionMixer) [129] indicates a cutting-edge direction
in slum research. These networks suggest that using multimodal data can lead
to a more nuanced representation of urban spaces, which could help with the
accuracy of classifying different types of urban forms.

  * •

Nairobi’s slums are among the most studied in Africa. These settlements are
characterized by high population densities, inadequate infrastructure, and
services. It has grappled with housing inadequacies, a legacy of its colonial
past that continues to challenge its urban landscape. The complexity of
Nairobi’s slums and socio-economic issues pose unique challenges to slum
mapping. Nairobi provides a stark representation of the quintessential African
urban slum, rich in community life but facing significant development
challenges. The studies here have not only focused on the mapping of slums but
also on predicting the perceptions of deprivation among citizens [108],
reflecting a multi-dimensional approach to understanding slum environments.
The emphasis on models that map deprivation from citizen votes in Nairobi
represents a shift towards more inclusive, community-focused urban research.
The ”EO + Morphometrics” methodology [117] highlights the significance of
urban morphology in satellite data interpretation, enabling more accurate and
reproducible urban pattern mapping.

The application of deep learning models across these regions is not just a
technological pursuit but also an attempt to incorporate socioeconomic
factors. The cross-regional study of these areas reveals intriguing patterns
and common challenges, emphasizing the need for technically robust and
socioeconomically responsive models for slum inhabitants. Mumbai and Shenzhen
focus on high-resolution imaging for detailed urban analysis, and Nairobi uses
a combination of VHR imagery and socio-economic data, demonstrating the
importance of community-focused research in urban planning. Tables IV, V, and
VI summarize the key findings from the predominant studies across these
regions, reflecting a range of methodologies and satellite data used. The
diversity in data sources, from satellites like WorldView and Pleiades to
Sentinel-2, underlines the balance required between detailed imagery and
spectral analysis. The comparison of model performance metrics across regions
reveals variations in focus and outcomes. Mumbai shows high accuracy in slum
segmentation, while Shenzhen’s models excel in integrating diverse data types
for urban analysis. Nairobi’s approach highlights the integration of deep
learning with citizen science to enhance the social relevance of technological
interventions. This geographical skew suggests an urgent need for solutions
tailored to the specific challenges of rapidly urbanizing regions. It also
raises concerns about data availability and representativeness, urging
researchers to consider local context and physical variances in their methods.
The discussion of these aspects in the context of each study provides a
comprehensive picture of deep learning applications in slum mapping and sets
the stage for future research focused on technological advancements as well as
socio-economic impacts.

TABLE IV: Key Studies in Deep Learning for Mumbai Slum Mapping. Year (Study Reference) |  Study Description |  Satellite used
(Resolution in m) |  Model employed |  Performance
metrics
---|---|---|---|---
2024 [106] |  This study introduced GASlumNet, a geoscience-aware network mapping accurate slum. It aimed to incorporate geoscientific knowledge, including spectral features and textural information. |  Spectrum-01 (5m) and Sentinel-2 (10m) |  GASlumNet, a combination of UNet and ConvNeXt |  OA: 94.89%
P: 0.7613
R: 0.7985
IoU: 0.6386
2023 [118] |  This study trained three modified CNN models on manually labeled high-resolution satellite imagery to identify green cover and open spaces within informal settlements. |  Pleiades-1A (0.5m) |  VGG16-UNet, MobileNetV2-UNet, and DeepLabV3+. |  VGG16-Unet (best):
OA: 95.02%
P: 0.9508
R: 0.9502
F1: 0.9502
2022 [120] |  This study developed an end-to-end, multi-channel supervised data fusion framework called GeoInFuse to classify different urban form typologies in heterogeneous hyper-dense cities. |  LISS IV (5.8m) with the Cartosat (1m) |  Pre-trained networks such as VGGNet, ResNet, and DenseNet within the GeoInFuse framework. |  GeoInFuse-ResNet (best):
OA: 74.98%
P: 0.739
R: 0.750
F1: 0.737
2020 [123] |  This study used contourlet transforms and U-net architecture to construct multiscale deep learning techniques for recognizing informal built-up areas. This strategy helps the model extract and use detailed textural and spatial characteristics. . |  Worldview-2 (2m) |  U-net (modified), augmented with contourlet masks of different sizes at different scales. |  OA: 94.98%
P: 0.9345
R: 0.9101
F1: 0.9221
mIoU: 0.89
2023 [126] |  This study evaluated the U-Net model for slum delineation and categorized it as slums or non-slums. |  Sentinel-2 (10m) |  U-Net |  OA: 98.2%
P: 0.973
R: 0.983
F1: 0.978
2019 [132] |  This study explored the application of transfer learning in fully convolutional networks (FCNs) for the semantic segmentation of slums. It leverages pre-trained networks on very high-resolution optical satellite imagery and transfers this learning to different satellite data types. |  QuickBird (0.5 m), Sentinel-2 (10 m), and TerraSAR-X (6m). |  Fully Convolutional Network (FCN) based on the VGG19 architecture |  Transfer Learning: Quickbird to Sentinel-2 (best): OA: 89.64%
K: 0.85
IoU: 0.8743
2019 [134] |  This study addressed the challenge of slum mapping in urban environments with highly imbalanced datasets using transfer learning in FCNs. |  QuickBird (2.4m); pan-sharpened to 0.6m |  Fully Convolutional Network (FCN) based on the VGG19 architecture |  IoU: 0.72
2019 [138] |  This study investigated transfer learning with pre-trained convolutional networks for mapping urban slums using VHR and medium-resolution satellite imagery. |  Pleiades-1A (0.50m) and Sentinel-2 (10m) |  Used pre-trained convolutional network (Inception-v3) |  For VHR Imagery (best):
OA:94.3%
K: 0.704
IoU: 0.583
2022 [139] |  This study introduced an approach notable for quantifying pixel-wise uncertainty in predictions, enhancing the interpretability and reliability of slum mapping efforts. |  Sentinel-2 (10m) |  U-Net model with Monte Carlo Dropout (MCD) for uncertainty quantification |  AUPRC score: 0.74
Uncertainty value: 9.7 x 10-9
TABLE V: Deep learning studies on mapping slum settlements in Shenzhen. Year (Study Reference) |  Study Description |  Satellite used
(Resolution in m) |  Model employed |  Performance
metrics
---|---|---|---|---
2022 [107] |  This study used remote and social sensing data to create a hierarchical recognition framework for fine-grained urban villages (UVs). The methodology improves the accuracy and interpretability of UV mapping by using a multi-scale approach that mimics human cognitive processes. |  SPOT-5 (2.5 m) |  A combination of machine learning classifiers and geospatial data analysis. |  OA: 96.23%
K: 0.920
2023 [111] |  This study presented a deep learning module that integrates varying numbers of street-level images to represent the characteristics of an urban spatial unit. This approach enables a comprehensive and representative understanding of urban spaces. |  Collected from Google Earth (0.6m) |  Vision-LSTM module: comprises a Convolutional Neural Network (CNN) with shared weights and a Recurrent Neural Network (RNN). |  OA: 91.6%
F1: 0.773
K: 0.720
2022 [119] |  This study focused on improving the semantic segmentation of urban informal settlements (UISs) using a transformer-based multimodal fusion network. The method leverages remote sensing images and building polygon data to achieve fine-scale mapping of UISs. |  Collected from Google Earth (1.19m) |  UisNet (transformer-based multimodal fusion network) |  OA: 94.80%
mIoU: 0.8551
2022 [129] |  This study introduced a multilevel spatial-channel feature fusion network to improve UV classification by integrating satellite and street view images. The method aims to overcome the limitations of single-modality data by fusing features at multiple spatial and channel levels. |  Collected from Google Earth (1.19m) |  FusionMixer (CNN-based feature extraction module) and a multilevel spatial-channel feature fusing layer. |  OA: 94.43%
K: 0.8757
2017 [141] |  This study introduced an unsupervised deep learning method for detecting UVs. This approach uses a deep CNN to autonomously learn feature representations, which is particularly valuable given the scarcity of labeled data in remote sensing applications. |  QuickBird (2.4m) |  An unsupervised deep neural network comprising an unsupervised deep convolutional neural network and an unsupervised deep fully connected neural network. |  OA: 98.55%
K: 0.9623
2022 [143] |  This study addressed the classification of UIS by integrating remote sensing images with time-series Tencent population density (TPD) data using a hybrid deep learning network, aiming to improve the accuracy of UIS classification by capturing both spatial features of geological objects and temporal features of human activity patterns. |  Collected from Google Earth (1.19m) |  STNet, which includes PDNet, ResMixer, and a Transformer-based spatial-temporal fusing layer. PDNet handles time-series TPD data, ResMixer processes VHR images, and the Transformer layer fuses these inputs. |  OA: 88.58%
K: 0.7716
TABLE VI: Deep learning studies on Nairobi’s urban slums. Year (Study Reference) |  Study Description |  Satellite used
(Resolution in m) |  Model employed |  Performance
metrics
---|---|---|---|---
2024 [108] |  This study developed methods for predicting citizens’ perceptions of deprivation using satellite imagery, citizen science, and AI. It explores AI’s ability to model perceptions of deprivation derived from votes by slum citizens, with deep learning outperforming conventional machine learning methods. These models aid in comprehending and predicting how locals perceive the physical aspects of urban deprivation. |  WorldView-3(1.20 m) |  2 different Convolutional Neural Networks (CNN): VGG and DenseNet-121. |  DenseNet-121 pretrained RGB bands (best):
Max R2: 0.841
Min RMSE: 0.693
Mean R2: 0.801
Mean RMSE: 0.767
Std R2: 0.021
Std RMSE: 0.039
2023 [117] |  This study presented ”EO + Morphometrics,” workflow to improve the scientific validity and reproducibility of urban pattern mapping using EO and urban morphometrics. This approach focuses on detecting physical urban elements from satellite imagery and then applying urban morphometrics to these elements to derive interpretable urban patterns. |  Collected from Google Earth (0.6 to 1.2m) |  U-Net with ResNet-50 as the encoder |  Accuracy: 93.79%
2022 [122] |  This study applied deep learning techniques and morphological analysis to identify degrees of deprivation in deprived urban areas (DUAs). |  WorldView-3(1.20 m) |  U-Net architecture for semantic segmentation |  F1: 0.84
IoU: 0.73
2024 [135] |  This study proposed a scalable and transferable method to map deprived areas across cities and countries in a consistent manner. |  Sentinel-2 (10m) |  Multi-Layer Perceptron (MLP), Random Forest (RF), Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost). |  MLP (best):
F1: 0.79
P: 0.98
R: 0.60
2022 [144] |  This study investigated the potential of VHR EO-based data to predict degrees of intra-urban deprivation. It involved a two-step workflow: characterizing multiple dimensions of deprivation using Principal Component Analysis (PCA) and predicting these degrees using a CNN. |  SPOT-7 (1.5m) |  CNN-based regression model |  R2: 0.6543
RMSE: 0.0582

###  VII-B Observations on Model Selection in Deep Learning for Slum Mapping

Our meta-analysis, which reviews 40 scholarly articles from 2014 to 2024,
highlights the application of deep learning in slum mapping. The meta-analysis
scrutinized articles selected through a meticulous process, leveraging
databases such as Web of Science, Scopus, and Science Direct. PRISMA protocols
guided this selection to ensure the integrity and relevance of the findings.
Our study revealed the region examined the most, varying data sources from VHR
satellites like WorldView to Sentinel satellites, and a diverse array of deep
learning applications tailored to these contexts. The diverse dataset
preparation and model application underscore the adaptability required to
manage the complexities of urban informal settings effectively. This meta-
analysis further emphasizes the need for context-specific model selection.
CNNs have performed well in places with complicated spatial features as they
can handle large amounts of data effectively [108, 109, 112, 115, 116, 121].
Alternatively, U-Nets excel at segmenting dense slum regions where precise
delineation of boundaries is essential [105, 113, 123, 126, 142]. Hybrid
models, combining features from various architectures, are promising for
combining data types, such as satellite imagery and urban data [106, 111, 114,
119, 143]. Furthermore, the Adam optimizer has been widely adopted due to its
efficiency in handling sparse gradients and adaptability to different data
modalities, making it suitable for the variable nature of slums in satellite
imagery. The WCE loss is prevalent due to its effectiveness in dealing with
class imbalances—a common issue in slum datasets where non-slum areas vastly
outnumber slum areas. The incorporation of Dice loss in hybrid loss functions
helps improve model performance on segmentation tasks by emphasizing spatial
overlap, which is crucial for accurate slum boundary detection. This study
also underscores the increasing integration of deep learning models with GIS,
which enhances spatial analysis capabilities. This integration is vital for
translating model outputs into actionable insights for urban planning and
policy-making, emphasizing the need for models that can seamlessly operate
within GIS platforms. Following our comprehensive review, several key
observations regarding model selection emerged:

  * •

Model Suitability Across Different Regions: Specific models, notably CNNs,
showed enhanced performance in regions with dense urban fabrics, where their
ability to segment detailed slum features proves invaluable.

  * •

Impact of Data Quality on Model Choice: The quality of data significantly
influences model effectiveness, with higher-resolution imagery favoring
complex models capable of detailed feature processing. In contrast, areas with
lower-resolution data might benefit from simpler models that require less
computational power.

  * •

Adaptability and Flexibility: The robustness of models incorporating transfer
learning or those pre-trained on diverse datasets highlights their suitability
for adapting to new, varied slum environments.

  * •

Technical Constraints and Resource Availability: Model selection often hinges
on available technical resources, with simpler models preferred in resource-
constrained settings to ensure broader accessibility.

  * •

Future Directions in Model Development: The integration of AI with GIS points
to future research directions focusing on hybrid models that merge deep
learning’s analytical strength with GIS’s spatial analysis capabilities.

This synthesis of our meta-analysis with focused observations provides a
robust framework for enhancing the precision and impact of deep learning in
slum mapping, guiding future urban planning and policy-making to be more data-
driven and inclusive.

###  VII-C Issues and possible solutions

The use of deep learning techniques in slum mapping through the analysis of EO
data presents significant potential for revolutionary outcomes. These
strategies possess the capacity to significantly augment our comprehension and
surveillance of informal settlements. Nevertheless, it is necessary to
acknowledge and tackle certain challenges to achieve optimal results.

  * •

Data Availability: The effectiveness of using deep learning techniques for
mapping slums is contingent upon extensive training data. The lack of
localized data [146] during the initial stages might significantly compromise
the dependability and precision of mapping solutions due to the varied
characteristics of slums. Without robust and diverse training datasets, models
cannot be expected to perform with high accuracy across various geographies.
Policymakers and funding agencies must recognize the need for and invest in
the collection of local data, which can significantly enhance the models’
utility.

  * •

Dataset Bias: The issue of dataset bias arises when considering the
representation of slums in satellite and EO data. Slums, characterized by
their informal and temporary nature, may not be sufficiently captured in these
datasets despite the ongoing availability of such data. The existence of
diverse slum areas across different locations [114] presents a significant
problem in acquiring the comprehensive dataset necessary for accurate training
and detection. Governments, NGOs, and academia can collaborate to develop more
representative datasets for more equitable and effective urban planning.

  * •

Explainability: The concept of explainability in the context of slum mapping
carries significant socio-economic and policy consequences. As these models
inform decisions that affect urban populations, especially the marginalized,
it’s essential that the models’ decision-making processes are transparent
[147]. This calls for interdisciplinary collaboration, where urban planners,
data scientists, and social workers come together to ensure the models are not
just effective but also just and fair.

  * •

Privacy and Ethical Concerns: The application of deep learning in urban
monitoring, particularly in the context of slum mapping, gives rise to privacy
and ethical concerns [148] that must be addressed. Improper use of data may
result in unwarranted monitoring and harm vulnerable individuals. Ethical
considerations [149] extend beyond the realm of privacy. An ethical concern
arises from the absence of informed consent among individuals residing in
slums, who are frequently unaware of being subjected to technological
research. The lack of transparency in their data collection, processing, and
use gives rise to ethical concerns. Furthermore, these studies have the
potential to stigmatize slums by spreading prejudices and disregarding their
intricate socio-economic dynamics. The ethical ramifications of data
interpretation in policymaking have similar significance. Urban planners who
make decisions based on deep learning algorithms should give priority to the
well-being and rights of residents to prevent the exacerbation of
marginalization or displacement.

The necessity of extensive labeled training datasets for slum mapping is
particularly evident in deep learning [150]. To ensure efficient model
training, high-quality and diverse data that precisely depicts slum patterns
and differences across geographical regions is essential. CNNs, known for
their spatial recognition performance, depend on the amount and detail of
training data, especially when dealing with intricate structures in slum
areas. Due to the complex nature of urban slums, it is imperative for models
to accurately identify and analyze minute details, which may require multiple
layers within the network [110], increasing the computational requirements.
So, finding the right balance between computing efficiency and algorithmic
depth becomes crucial. Overly complex networks might capture noise instead of
meaningful patterns, potentially leading to overfitting and misclassification.
The importance of deep learning models lies in their adaptability. The
characteristics of slums in a city or country might exhibit substantial
variations compared to those in other locations [13, 14]. The applicability of
models trained on a particular dataset, such as slums in Mumbai, to slums in
Nairobi may be limited due to localized differences in structural
characteristics, building materials, and spatial arrangement. The use of deep
learning in slum mapping through the analysis of EO data holds significant
potential. However, achieving this potential depends on effectively overcoming
the inherent obstacles associated with this approach. The effective and
responsible use of deep learning techniques for urban monitoring and planning
necessitates a comprehensive comprehension of slums and the implementation of
robust data techniques and transparent models. It is also imperative to
establish and adhere to rigorous ethical standards for data collection [151],
analysis, and use to tackle these difficulties. The emphasis should be on
prioritizing privacy and ensuring data security when establishing these
standards. Involving local communities in the process of data collection
guarantees that their perspectives are considered [152], fostering an
inclusive and ethical methodology. Ethicists, sociologists, community leaders,
technologists, and urban planners might collaborate to gain a more
comprehensive understanding of the ethical implications associated with the
use of deep learning in urban monitoring.

The use of deep learning for slum mapping poses complex issues. One of the
primary challenges lies in accurately defining and describing it [13]. To
overcome this challenge, it is crucial to integrate satellite imagery with
street-level data. This will provide not only a perspective on individual slum
structures but also on the broader urban framework. The use of resources such
as OpenStreetMap (OSM) [153], and Google Street View [124] enriches data
collection with comprehensive geographic information and panoramic street-
level visuals. The crowd-sourced OSM data and the Google Street View images
are beneficial for thoroughly investigating slums, as they supply a vast
amount of information about urban layouts and provide detailed views of the
urban environment. A progressively encouraging approach in this domain is
generating synthetic training data that accurately replicates urban
environments, including the intricate and diverse characteristics of slums.
There are numerous benefits to using synthetic data [154, 155]. It offers a
cost-efficient alternative to gathering a large amount of real-world data.
Moreover, it can be generated in significant quantities, covering a diverse
range of situations and structural differences, which is essential for
effectively training deep learning models. Due to its lack of specific data
acquisition restrictions, this method significantly reduces the biases
frequently present in conventional datasets. Synthetic datasets can also be
tailored to emphasize specific attributes of slum regions, guaranteeing that
models are adequately prepared to handle the wide range of variations and
unique features in these settings. Combining satellite imagery with street-
level data and the smart use of synthetic training data offers a complete and
multi-dimensional method for mapping urban slums. This methodology overcomes
the difficulties posed by the intricate characteristics of these environments
and enhances the potential of deep learning models by offering them varied,
abundant, and impartial data for efficient training.

Regarding the choice of predictive models, it is important to represent the
complex urban spatial structures in the feature maps. Vision Transformers
(ViT), a computational model that analyses image patches and identifies
distant relationships, can highlight the distinctive characteristics of urban
design [156, 157, 158, 159]. Moreover, with the growing complexity of models,
their reliability is an important concern. Using models that are very good at
transferring across a wide range of image sources and conditions, including
explainability tools, can help us understand how these models make decisions
and trust their outcomes.

While our study provides comprehensive insights into the use of deep learning
for slum mapping, it is important to acknowledge certain limitations. The
effectiveness of the models depends on the quality and diversity of the
training datasets, which may not always be available or represent all types of
slum environments. Future research should focus on developing techniques for
generating synthetic datasets that can accurately reflect the complex nature
of slums. Additionally, integrating newer architectures and hybrid models
could improve accuracy and adaptability in diverse urban contexts as deep
learning evolves. There’s also a need for studies focusing on technological
advancement and considering the socio-economic implications, ensuring that
these technologies are used responsibly and ethically.

###  VII-D Limitations of Remote Sensing in Slum Mapping

One of the fundamental challenges in the application of remote sensing for
slum detection and mapping lies in the conceptual ambiguity surrounding the
definition of slums [160]. There is a wide diversity in their appearance, both
locally and globally [161]. This leads to differences in assessments of a
slum’s boundaries. Such ambiguities make it difficult to train and validate
algorithms, impacting their geographic, contextual, and temporal
transferability. Moreover, a gap often exists between the remote sensing
community and users in understanding the data requirements for different user
groups and the capabilities and limitations of remote sensing. This disconnect
hinders the production of maps suitable for diverse user groups, including
local and global policymakers.

The limitations of remote sensing in slum detection and mapping become more
pronounced when considering the difficulties in accessing basic amenities like
sanitation and water [162]. Remote sensing provides a valuable perspective on
the physical layout and extent of slums but lacks the capability to directly
assess the availability and quality of essential services such as clean water
and sanitation facilities. This limitation is significant, as these aspects
are crucial for understanding the living conditions within slums. Remote
sensing data predominantly captures topographical and physical features
visible from space, but detailed information about the internal conditions of
buildings, the quality of water sources, and the presence of sanitation
facilities require ground-based assessments or other data sources. This gap
means that while satellite imagery can identify areas likely to be slums based
on physical characteristics, it cannot provide comprehensive insight into the
quality of life or the health risks associated with inadequate access to clean
water and proper sanitation.

The lack of high-resolution data in some regions exacerbates this challenge.
Even where high-resolution imagery is available, distinguishing between
different types of small-scale infrastructure, like water taps or toilets, is
incredibly challenging. A combination of remote sensing data with other data
sources, such as ground surveys, census data, and participatory mapping with
local communities, is essential to address these issues [163]. This multi-
source approach enhances the accuracy of slum mapping and provides a more
holistic view of the living conditions within these communities. By overcoming
these challenges, remote sensing can significantly contribute to the
understanding and improvement of living conditions in slums, aiding global and
local efforts in urban planning and policy-making.

##  VIII Conclusion

The rapid progress of deep learning has resulted in significant breakthroughs
in the domain of slum mapping using remote sensing data. This study
consolidates findings from 40 studies between 2014 and 2024, picked from a
database of scholarly contributions. The cumulative results of these studies
provide a comprehensive portrayal of the transformative impact of deep
learning methods on our capacity to identify, examine, and comprehend slums.

The dynamic nature of CNN evolution is shown to have important consequences
for slum mapping. Considering the intricate and dynamic characteristics of
urban slums, it is imperative for researchers and urban planners to stay
updated about the current deep-learning approaches. Nevertheless, there is a
growing consensus among the community that, rather than continuously
developing new architectural designs, there is considerable value in enhancing
and optimizing current models to address the obstacles associated with slum
mapping [14]. The study continues by expressing an optimistic perspective,
stating that CNNs are poised to play a leading role in the future of slum
mapping. This advancement is expected to enable more urban insights, building
upon the current successes in this field. Within the realm of slum mapping,
data serves as a representation of the complex socio-spatial structure, and
the core elements for achieving success in deep learning involve ensuring the
granularity, quality, and representativeness of the data. The implementation
of a data-centric approach has the potential to effectively address the
disparity between advanced models and the practical challenges faced in slum
areas. The advancements in deep learning for slum mapping have direct
implications for urban development strategies. Accurate mapping can inform
infrastructure development, resource allocation, and service delivery in
informal settlements. Policymakers can utilize these models to monitor urban
growth, plan sustainable development projects, and prioritize interventions in
the neediest areas. This meta-analysis also suggests areas where further
research could be beneficial, such as developing models that are better
tailored to local conditions or that can integrate data from a variety of
sources. Policymakers can use these findings to guide research funding towards
these areas, ensuring that future developments continue to improve the quality
and utility of slum maps.

This study culminates with a message of optimism, envisioning a future where
deep learning is integral to our understanding and improvement of urban
environments. For policymakers, this means supporting research initiatives
that bridge the gap between high-level models and on-the-ground urban
challenges. For practitioners, it means embracing these tools to foster more
informed, evidence-based decision-making. In conclusion, as we harness the
power of CNNs for slum mapping, we are on the verge of a new era in urban
planning—one where technology enables us to see the unseen and act with
greater precision for the betterment of all urban inhabitants.

## References

  * [1] UN DESA. 2022. The Sustainable Development Goals Report 2022—July 2022. New York, USA: UN DESA. © UN DESA. https://unstats.un.org/sdgs/report/2022/
  * [2] United Nations Human Settlements Programme (UN-Habitat), World Cities Report 2022: Envisaging the Future of Cities, 2022
  * [3] UNDP (United Nations Development Programme). 2022. Human Development Report 2021-22: Uncertain Times, Unsettled Lives: Shaping our Future in a Transforming World. New York
  * [4] World Bank. _Urban Development Overview_. World Bank, 2021. https://www.worldbank.org/en/topic/urbandevelopment/overview.
  * [5] United-Nations. World Urbanization Prospects 2018
  * [6] The Sustainable Development Goals Report 2023: Goal 11, United Nations Statistics Division, 2023. https://unstats.un.org/sdgs/report/2023/goal-11/
  * [7] UN-Habitat, Urban Indicators Database, Housing, Slums, and Informal Settlements, 2021. https://data.unhabitat.org/pages/housing-slums-and-informal-settlements
  * [8] UN-Habitat, Habitat III issue paper 22—informal settlements. UN Habitat, New York. (2015).
  * [9] E. Damte, B.O. Manteaw, and C. Wrigley-Asante, Urbanization, climate change and health vulnerabilities in slum communities in Ghana, The Journal of Climate Change and Health, vol. 10, p.100189, 2023.
  * [10] O.B. Adegun, Climatic disasters within a flood-prone coastal slum in Lagos: coping capacities and adaptation prospects. International Journal of Disaster Resilience in the Built Environment, vol. 14, no. 2, pp.212-228, 2023.
  * [11] H. Akther, and M.M. Ahmad, Livelihood in the pluvial flood prone slum communities in Dhaka, Bangladesh. Progress in Disaster Science, vol. 14, p.100227, 2022.
  * [12] A. Abascal, N. Rothwell, A. Shonowo, D.R. Thomson, P. Elias, H. Elsey, G. Yeboah AND M. Kuffer, ”Domains of deprivation framework” for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review. Computers, environment and urban systems, vol. 93, p. 101770, 2022.
  * [13] M. Kuffer, K. Pfeffer, and R. Sliuzas, Slums from space—15 years of slum mapping using remote sensing. Remote Sensing, vol. 8, no. 6, p.455, 2016.
  * [14] R. Mahabir, A. Croitoru, A.T. Crooks, P. Agouris and A. Stefanidis, A critical review of high and very high-resolution remote sensing approaches for detecting and mapping slums: Trends, challenges and emerging opportunities. Urban Science, vol. 2, no. 1, p.8, 2018.
  * [15] R. Mahabir, P. Agouris, A. Stefanidis, A. Croitoru and A.T. Crooks. Detecting and mapping slums using open data: A case study in Kenya. International Journal of Digital Earth, vol. 13, no. 6, pp.683-707, 2020.
  * [16] S. R. Maiya and S. C. Babu, Slum segmentation and change detection: A deep learning approach. arXiv preprint arXiv:1811.07896, 2018.
  * [17] O. Kit and M. Lüdeke. Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery. ISPRS journal of photogrammetry and remote sensing, vol. 83, pp.130-137, 2013.
  * [18] R. Liu, M. Kuffer and C. Persello. The temporal dynamics of slums employing a CNN-based change detection approach. Remote sensing, vol. 11, no. 23, pp.2844, 2019.
  * [19] Kenya Slum Upgrading Programme. The Case Of Kibera Slum Upgrading Project, Kenya. https://tinyurl.com/48t4mb4r
  * [20] T. Meredith and M. MacDonald. Community-supported slum-upgrading: innovations from Kibera, Nairobi, Kenya. Habitat International, vol. 60, pp. 1-9, 2017.
  * [21] Shelter Associates, https://shelter-associates.org/
  * [22] D.R. Thomson, M. Kuffer, G. Boo, B. Hati, T. Grippa, H. Elsey, C. Linard, R. Mahabir, C. Kyobutungi, J. Maviti and D. Mwaniki. Need for an integrated deprived area “slum” mapping system (IDEAMAPS) in low-and middle-income countries (LMICs). Social Sciences, vol. 9, no. 5, p.80, 2022.
  * [23] Slummap, https://slummap.net/
  * [24] X. X Zhu, D. Tuia, L. Mou, G. S. Xia, L. Zhang, F. Xu, and F. Fraundorfer. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, vol. 5, no.4, pp. 8–36, 2017. IEEE.
  * [25] X. Yuan, J. Shi, J.and L. Gu. A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, vol. 169, pp. 114417, 2021.
  * [26] I.S. Baud, K. Pfeffer, N. Sridharan and N. Nainan. Matching deprivation mapping to urban governance in three Indian mega-cities. Habitat International, vol. 33, no. 4, pp.365-377, 2009.
  * [27] J.R. Weeks, A. Hill, D. Stow, A. Getis and D. Fugate. Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, vol. 69, pp.9-22, 2007.
  * [28] K. Jacobsen, G. Buyuksalih and I. Baz. Mapping from space for developing countries. In Proc. EARSeL Joint Workshop: Remote Sensing—New Challenges of High Resolution, pp. 104-114, 2008.
  * [29] M. Kuffer, K. Pfeffer, R. Sliuzas and I. Baud. Extraction of slum areas from VHR imagery using GLCM variance. IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 9, no. 5, pp.1830-1840, 2016.
  * [30] C.M. Gevaert, C. Persello, R. Sliuzas and G. Vosselman. Informal settlement classification using point-cloud and image-based features from UAV data. ISPRS journal of photogrammetry and remote sensing, vol. 125, pp.225-236, 2017.
  * [31] S.C.L. Ribeiro, M. Jarzabek-Rychard, J.P. Cintra and H.G. Maas. Describing the vertical structure of informal settlements on the basis of lidar data–a case study for favelas (slums) in Sao Paulo city . ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 4, pp.437-444, 2019.
  * [32] M. S. Moeller. Remote Sensing for the monitoring of urban growth patterns. world, vol.1975, p.2000, 1950.
  * [33] D. Fugate, E. Tarnavsky and D. Stow. A survey of the evolution of remote sensing imaging systems and urban remote sensing applications. Remote sensing of urban and suburban areas, pp. 119-139, 2010.
  * [34] C. Ioannidis, C. Psaltis and C. Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Computers, Environment and Urban Systems, vol. 33, no. 1, pp.64-74, 2009.
  * [35] E. Aptoula and S. Lefèvre. Morphological texture description of grey-scale and color images. In Advances in imaging and electron physics, vol. 169, pp. 1-74, 2011.
  * [36] R.M. Haralick, K. Shanmugam and I.H. Dinstein. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, no. 6, pp.610-621, 1973.
  * [37] A.M. Shabat and J.R. Tapamo. A comparative study of the use of local directional pattern for texture-based informal settlement classification. Journal of applied research and technology, vol. 15, no. 3, pp.250-258, 2017.
  * [38] M. Wurm, M. Weigand, A. Schmitt, C. Geiß and H. Taubenböck. Exploitation of textural and morphological image features in Sentinel-2A data for slum mapping. In 2017 Joint Urban Remote Sensing Event (JURSE), pp. 1-4, 2017.
  * [39] R. Prabhu and R.A. Alagu Raja. Urban slum detection approaches from high-resolution satellite data using statistical and spectral based approaches. Journal of the Indian Society of Remote Sensing, vol. 46, pp.2033-2044, 2018.
  * [40] J. Wang, M. Kuffer and K. Pfeffer. The role of spatial heterogeneity in detecting urban slums. Computers, environment and urban systems, vol. 73, pp.95-107, 2019.
  * [41] R. Prabhu, B. Parvathavarthini and R.A. Alagu Raja. Slum extraction from high resolution satellite data using mathematical morphology based approach. International Journal of Remote Sensing, vol. 42, no. 1, pp.172-190, 2021.
  * [42] J. Graesser, A. Cheriyadat, R.R. Vatsavai, V. Chandola, J. Long and E. Bright. Image based characterization of formal and informal neighborhoods in an urban landscape. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 4, pp.1164-1176, 2012.
  * [43] M. Gholoobi and L. Kumar. Using object-based hierarchical classification to extract land use land cover classes from high-resolution satellite imagery in a complex urban area. Journal of Applied Remote Sensing, vol. 9, no. 1, pp.096052-096052, 2015.
  * [44] P. Hofmann. Detecting informal settlements from IKONOS image data using methods of object oriented image analysis-an example from Cape Town (South Africa). Jürgens, C.(Ed.): Remote Sensing of Urban Areas/Fernerkundung in urbanen Räumen, pp.41-42, 2001.
  * [45] M. Baatz, M. Heynen, P. Hofmann, I. Lingenfelder, M. Mimier, A. Schape, M. Weber and G. Willhauck. eCognition User Guide 2.0: Object oriented image analysis. Definiens Imaging GmbH, Munich, Germany, vol. 427, 2001.
  * [46] P. Hofmann, J. Strobl, T. Blaschke and H. Kux. Detecting informal settlements from QuickBird data in Rio de Janeiro using an object based approach. Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications, pp.531-553, 2008.
  * [47] S. Niebergall, A. Loew and W. Mauser. Object-oriented analysis of very high-resolution QuickBird data for mega city research in Delhi/India. In 2007 Urban Remote Sensing Joint Event, pp. 1-8, 2007.
  * [48] T. Veljanovski, U. Kanjir, P. Pehani, K. Oštir and P. Kovačič. Object-based image analysis of VHR satellite imagery for population estimation in informal settlement Kibera-Nairobi, Kenya. Remote sensing–applications, pp.407-434, 2012.
  * [49] D. Kohli, R. Sliuzas, N. Kerle and A. Stein. An ontology of slums for image-based classification. Computers, environment and urban systems, vol. 36, no. 2, pp.154-163, 2012.
  * [50] D. Kohli, R. Sliuzas and A. Stein. Urban slum detection using texture and spatial metrics derived from satellite imagery. Journal of spatial science, vol. 61, no. 2, pp.405-426, 2016.
  * [51] M.K. Ridd. Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International journal of remote sensing, vol. 16, no. 12, pp.2165-2185, 1995.
  * [52] N. Williams, D. Quincey and J. Stillwell. Automatic classification of roof objects from aerial imagery of informal settlements in Johannesburg. Applied Spatial Analysis and Policy, vol. 9, pp.269-281, 2016.
  * [53] N. Mudau and P. Mhangara. Investigation of informal settlement indicators in a densely populated area using very high spatial resolution satellite imagery. Sustainability, vol. 13, no. 9, pp.4735, 2021.
  * [54] S. Georganos, A. Abascal, M. Kuffer, J. Wang, M. Owusu, E. Wolff and S. Vanhuysse. ”Is it all the same? Mapping and characterizing deprived urban areas using Worldview-3 superspectral imagery. A case study in Nairobi, Kenya.” Remote Sensing, vol. 13, no. 24, pp.4986, 2021.
  * [55] F. Galeon. Estimation of population in informal settlement communities using high resolution satellite image. In XXI ISPRS Congress, Commission IV. Beijing, vol. 37, no. Part B4, pp. 1377-1381, 2008.
  * [56] T. Novack and H.J.H. Kux. Urban land cover and land use classification of an informal settlement area using the open-source knowledge-based system InterIMAGE. Health, Risk & Society, vol. 55, no. 1, pp.23-41, 2010.
  * [57] K.K. Owen and D.W. Wong. An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics. Applied Geography, vol. 38, pp.107-118, 2013.
  * [58] T. Kemper, N. Mudau, P. Mangara and M. Pesaresi. Towards an automated monitoring of human settlements in South Africa using high resolution SPOT satellite imagery.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, pp.1389-1394, 2015.
  * [59] J.C. Duque, J.E. Patino and A. Betancourt. Exploring the potential of machine learning for automatic slum identification from VHR imagery. Remote Sensing, vol. 9, no. 9, pp.895, 2017.
  * [60] M. Kuffer, K. Pfeffer, R. Sliuzas, I. Baud and M. Van Maarseveen. Capturing the diversity of deprived areas with image-based features: The case of Mumbai. Remote sensing, vol. 9, no. 4, pp.384, 2017.
  * [61] G. Leonita, M. Kuffer, R. Sliuzas and C. Persello. Machine learning-based slum mapping in support of slum upgrading programs: The case of Bandung City, Indonesia. Remote sensing, vol. 10, no. 10, pp.1522, 2018.
  * [62] E. Ranguelova, B. Weel, D. Roy, M. Kuffer, K. Pfeffer and M. Lees. Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India. European journal of remote sensing, vol. 52, no. sup1, pp.40-61, 2019.
  * [63] R. Prabhu, B. Parvathavarthini and A.R. Alaguraja. Integration of deep convolutional neural networks and mathematical morphology-based postclassification framework for urban slum mapping. Journal of Applied Remote Sensing, vol. 15, no. 1, pp.014515-014515, 2021.
  * [64] A. Schmitt, T. Sieg, M. Wurm and H. Taubenböck. Investigation on the separability of slums by multi-aspect TerraSAR-X dual-co-polarized high resolution spotlight images based on the multi-scale evaluation of local distributions. International journal of applied earth observation and geoinformation, vol. 64, pp.181-198, 2018.
  * [65] M. Wurm, H. Taubenböck, M. Weigand and A. Schmitt. Slum mapping in polarimetric SAR data using spatial features. Remote sensing of environment, vol. 194, pp.190-204, 2017.
  * [66] F. Lai and X. Yang. Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city. GIScience & Remote Sensing, vol. 57, no. 6, pp.830-844, 2020.
  * [67] J. Friesen, H. Taubenböck, M. Wurm and P.F. Pelz. The similar size of slums. Habitat International, vol. 73, pp.79-88, 2018.
  * [68] J. Friesen, H. Taubenböck, M. Wurm and P.F. Pelz. Size distributions of slums across the globe using different data and classification methods. European Journal of Remote Sensing, vol. 52, no. sup2, pp.99-111, 2019.
  * [69] D. Kohli, A. Stein and R. Sliuzas. Uncertainty analysis for image interpretations of urban slums. Computers, Environment and Urban Systems, vol. 60, pp.37-49, 2016.
  * [70] F. da Fonseca Feitosa, V.V. Vasconcelos, C.M.D. de Pinho, G.F.G. da Silva, G. da Silva Gonçalves, L.C.C. Danna and F.S. Lisboa. IMMerSe: An integrated methodology for mapping and classifying precarious settlements. Applied geography, vol. 133, pp.102494, 2021.
  * [71] M. Kuffer, S. Vanhuysse, S. Georganos and J. Wang. Meeting user requirements for mapping and characterizing deprived urban areas in support of pro-poor policies. GI_Forum, vol. 9, no. 1, pp.85-93, 2021.
  * [72] Z. Assarkhaniki, S. Sabri and A . Rajabifard. Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement. Big Earth Data, vol. 5, no. 4, pp.497-526, 2021.
  * [73] M.R. Ibrahim, J. Haworth and T. Cheng. URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision. Environment and Planning B: Urban Analytics and City Science, vol. 48, no. 1, pp.76-93, 2021.
  * [74] B.D. dos Santos, C.M.D. de Pinho, G.E.T. Oliveira, T.S. Korting, M.I.S. Escada and S. Amaral. Identifying precarious settlements and urban fabric typologies based on GEOBIA and Data mining in Brazilian Amazon Cities. Remote Sensing, vol. 14, no.3, p.704, 2022.
  * [75] K.N. Priyadarshini, V. Sivashankari and S. Shekhar. Identification of Urban Slums Using Classification Algorithms—A Geospatial Approach. In Proceedings of UASG 2019: Unmanned Aerial System in Geomatics 1, pp. 237-252, 2020.
  * [76] M. Farooq, G. Meraj, Rishabh, S. Kanga, R. Nathawat, S.K. Singh and V. Ranga, V. Slum Categorization for Efficient Development Plan—A Case Study of Udhampur City, Jammu and Kashmir Using Remote Sensing and GIS. Geospatial Technology for Landscape and Environmental Management: Sustainable Assessment and Planning, pp.283-299, 2022.
  * [77] A. Fallatah, S. Jones and D. Mitchell. Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study. International Journal of Remote Sensing, vol. 41, no. 11, pp.4421-4445, 2020.
  * [78] I. Goodfellow, Y. Bengio, A. Courville. Deep learning, 2016.
  * [79] MIT Technology Review. (2013). 10 breakthrough technologies 2013. [Online]. Available: https://www.technologyreview.com/technology/deep-learning/
  * [80] N. Kussul, M. Lavreniuk, S. Skakun and A. Shelestov. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp.778-782, 2017.
  * [81] P. Helber, B. Bischke, A. Dengel and D. Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp.2217-2226, 2019.
  * [82] A. Vali, S. Comai and M. Matteucci. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, vo. 12, no. 15, p.2495, 2022.
  * [83] K. Li, G. Wan, G. Cheng, L. Meng and J. Han. Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS journal of photogrammetry and remote sensing, vol. 159, pp.296-307, 2022.
  * [84] Z.Q. Zhao, P. Zheng, S.T. Xu and X. Wu. Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems, vol. 30, no. 11, pp.3212-3232, 2019.
  * [85] S.A. Mohamed, A.S. Mahmoud, M.S. Moustafa, A.K. Helmy and A.H. Nasr. Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN. International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, 2022.
  * [86] D. Wen, X. Huang, F. Bovolo, J. Li, X. Ke, A. Zhang and J.A. Benediktsson. Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp.68-101, 2021.
  * [87] Y. Wang, L. Gao, D. Hong, J. Sha, L. Liu, B. Zhang, X. Rong and Y. Zhang. Mask DeepLab: End-to-end image segmentation for change detection in high-resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation, vol. 104, pp.102582, 2021.
  * [88] C. Ma, L. Weng, M. Xia, H. Lin, M. Qian and Y. Zhang. Dual-branch network for change detection of remote sensing image. Engineering Applications of Artificial Intelligence, vol. 123, pp.106324, 2023.
  * [89] LeCun, Y., Bengio, Y. and Hinton, G., Deep learning. nature, vol. 521, no. 7553, pp.436-444, 2015.
  * [90] K. Jarrett, K. Kavukcuoglu, M.AA Ranzato and Y. LeCun, What is the best multi-stage architecture for object recognition? 2009 IEEE 12th international conference on computer vision, pp. 2146-2153, IEEE, 2009.
  * [91] Y.L. Boureau, J. Ponce, and Y. LeCun. A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th international conference on machine learning (ICML-10), pp. 111-118, 2010.
  * [92] A. Krizhevsky, I. Sutskever and G.E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, vol. 25, 2012.
  * [93] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  * [94] L. He, X. Zhang, S. Ren, and J. Sun. ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
  * [95] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 3431-3440, 2015.
  * [96] M.Gong, H. Yang and P. Zhang. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 129, pp. 212-225, 2017.
  * [97] J. Zabalza, J. Ren, J. Zheng, H. Zhao, C. Qing, Z. Yang, P. Du and S. Marshall. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, vol. 185, pp. 1-10, 2016.
  * [98] S. Hao, W. Wang, Y. Ye, T. Nie and L. Bruzzone. Two-stream deep architecture for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 4, pp.2349-2361, 2017.
  * [99] G.V. Glass. Primary, secondary, and meta-analysis of research. Educational researcher, vol. 5, no. 10, pp.3-8, 1976.
  * [100] M. Egger, G.D. Smith and A.N. Phillips. Meta-analysis: principles and procedures. Bmj, vol. 315, no.7121, pp.1533-1537, 1997.
  * [101] D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman and PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine, vol. 151, no. 4, pp.264-269, 2009.
  * [102] W.R. King and J. He. Understanding the role and methods of meta-analysis in IS research. Communications of the Association for Information Systems, vol. 16, no. 1, pp.32, 2005.
  * [103] R. Khatami, G. Mountrakis and S.V. Stehman. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote sensing of environment, vol. 177, pp.89-100, 2016.
  * [104] G. Grekousis. Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis. Computers, Environment and Urban Systems, vol. 74, pp.244-256, 2019.
  * [105] M. Wahbi, I. El Bakali, B. Ez-zahouani, R. Azmi, A. Moujahid, M. Zouiten, O.Y. Alaoui, H. Boulaassal, M. Maatouk and O. El Kharki. A deep learning classification approach using high spatial satellite images for detection of built-up areas in rural zones: Case study of Souss-Massa region-Morocco. Remote Sensing Applications: Society and Environment, vol. 29, p.100898, 2023.
  * [106] W. Lu, Y. Hu, F. Peng, Z. Feng and Y. Yang. A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping. Remote Sensing, vol. 16, no. 2, p.260, 2024.
  * [107] D. Chen, W. Tu, R. Cao, Y. Zhang, B. He, C. Wang, T. Shi and Q. Li. A hierarchical approach for fine-grained urban villages recognition fusing remote and social sensing data. International Journal of Applied Earth Observation and Geoinformation, vol. 106, p.102661, 2022.
  * [108] A. Abascal, S. Vanhuysse, T. Grippa, I. Rodriguez-Carreño, S. Georganos, J. Wang, M. Kuffer, P. Martinez-Diez, M. Santamaria-Varas and E. Wolff. AI perceives like a local: predicting citizen deprivation perception using satellite imagery. npj Urban Sustainability, vol. 4, no. 1, p.20, 2024.
  * [109] T. El Moudden and M. Amnai. Building an efficient convolution neural network from scratch: A case study on detecting and localizing slums. Scientific African, vol. 20, p. e01612, 2023.
  * [110] Y.A. Lumban-Gaol, A. Rizaldy and A. Murtiyoso. Comparison of Deep Learning Architectures for the Semantic Segmentation of Slum Areas from Satellite Images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, vol. 48, no. 1/W2-2023, pp.1439-1444, 2023.
  * [111] Y. Huang, F. Zhang, Y. Gao, W. Tu, F. Duarte, C. Ratti and Y. Liu. Comprehensive urban space representation with varying numbers of street-level images. Computers, Environment and Urban Systems, vol. 106, pp. 102043, 2023.
  * [112] C. Persello and A. Stein, A. Deep fully convolutional networks for the detection of informal settlements in VHR images. IEEE geoscience and remote sensing letters, vol. 14, no. 12, pp.2325-2329, 2017.
  * [113] J. Wang, M. Kuffer, D. Roy and K. Pfeffer. Deprivation pockets through the lens of convolutional neural networks. Remote sensing of environment, vol. 234, p.111448, 2019.
  * [114] T. Stark, M. Wurm, X.X. Zhu and H. Taubenböck, H., 2023, May. Detecting challenging urban environments using a few-shot meta-learning approach. In 2023 Joint Urban Remote Sensing Event (JURSE), pp. 1-4, 2023.
  * [115] H. Debray, M. Kuffer, C. Persello, C. Klaufus and K. Pfeffer. Detection of informal graveyards in lima using fully convolutional network with VHR images. In 2019 Joint Urban Remote Sensing Event (JURSE), pp. 1-4, 2019.
  * [116] N. Mboga, C. Persello, J.R. Bergado and A. Stein. Detection of informal settlements from VHR images using convolutional neural networks. Remote sensing, vol. 9, no. 11, pp.1106, 2017.
  * [117] J. Wang, M. Fleischmann, A. Venerandi, O. Romice, M. Kuffer and S. Porta. EO+ Morphometrics: Understanding cities through urban morphology at large scale. Landscape and Urban Planning, vol. 233, pp.104691, 2023.
  * [118] A. Dabra and V. Kumar. Evaluating green cover and open spaces in informal settlements of Mumbai using deep learning. Neural Computing and Applications, pp.1-16, 2023.
  * [119] R. Fan, F. Li, W. Han, J. Yan, J. Li and L. Wang. Fine-scale urban informal settlements mapping by fusing remote sensing images and building data via a transformer-based multimodal fusion network. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp.1-16, 2022.
  * [120] R. Bardhan, P. Gupta and A. Majumdar. GeoInFuse-A data-driven information fusion for intra-urban form classification in data-scarce heterogeneous cities. Cities, vol. 127, pp.103762, 2022.
  * [121] A. Ajami, M. Kuffer, C. Persello and K. Pfeffer. Identifying a slums’ degree of deprivation from VHR images using convolutional neural networks. Remote Sensing, vol. 11, no. 11, pp.1282, 2019.
  * [122] A. Abascal, I. Rodríguez-Carreño, S. Vanhuysse, S. Georganos, R. Sliuzas, E. Wolff and M. Kuffer. Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas. Computers, environment and urban systems, vol. 95, pp. 101820, 2022.
  * [123] R. A. Ansari, R. Malhotra and K. M. Buddhiraju. Identifying informal settlements using contourlet assisted deep learning. Sensors, vol. 20, no. 9, pp.2733, 2020.
  * [124] A. Najmi, C.M. Gevaert, D. Kohli, M. Kuffer and J. Pratomo. Integrating remote sensing and street view imagery for mapping slums. ISPRS International Journal of Geo-Information, vol. 11, no. 12, pp.631, 2022.
  * [125] K.K. Gadiraju, R.R. Vatsavai, N. Kaza, E. Wibbels and A. Krishna. Machine learning approaches for slum detection using very high resolution satellite images. In 2018 IEEE International Conference on data Mining Workshops (ICDMW), pp. 1397-1404, 2018.
  * [126] A. Raj, S. Agrawal, A. Mitra and M. Sinha. Mapping Slums from Satellite Imagery Using Deep Learning. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 6584-6587, 2023.
  * [127] A. Mattos, M. Bertolotto and G. McArdle. Mapping Slums with Deep Learning Feature Extraction, 2022.
  * [128] M.F.U. Rehman, I. Aftab, W. Sultani and M. Ali. Mapping temporary slums from satellite imagery using a semi-supervised approach. IEEE Geoscience and Remote Sensing Letters, vol. 19, pp.1-5, 2022.
  * [129] R. Fan, J. Li, F. Li, W. Han and L. Wang. Multilevel spatial-channel feature fusion network for urban village classification by fusing satellite and streetview images. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp.1-13, 2022.
  * [130] T. El Moudden, M. Amnai, A. Choukri, Y. Fakhri and G. Noreddine. New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city-Morocco. Scientific African, pp.e02135, 2024.
  * [131] T. Stark, M. Wurm, X. X. Zhu and H. Taubenböck. Satellite-based mapping of urban poverty with transfer-learned slum morphologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp.5251-5263, 2020.
  * [132] M. Wurm, T. Stark, X. X. Zhu, M. Weigand and H. Taubenböck. Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS journal of photogrammetry and remote sensing, vol. 150, pp. 59-69, 2019.
  * [133] T. El Moudden, R. Dahmani, M. Amnai and A.A. Fora. Slum image detection and localization using transfer learning: a case study in Northern Morocco. International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 3, pp.3299-3310, 2023.
  * [134] T. Stark, M. Wurm, H. Taubenböck and X.X. Zhu. Slum mapping in imbalanced remote sensing datasets using transfer learned deep features. In 2019 Joint Urban Remote Sensing Event (JURSE), pp. 1-4, 2019.
  * [135] M. Owusu, A. Nair, A. Jafari, D. Thomson, M. Kuffer and R. Engstrom. Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning. Computers, Environment and Urban Systems, vol. 109, p.102075, 2024.
  * [136] C. Persello and M. Kuffer. Towards uncovering socio-economic inequalities using VHR satellite images and deep learning. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 3747-3750, 2020.
  * [137] M. Owusu, M. Kuffer, M. Belgiu, T. Grippa, M. Lennert, S. Georganos and S. Vanhuysse, S. Towards user-driven earth observation-based slum mapping. Computers, environment and urban systems, vol. 89, pp.101681, 2021.