# Building Footprint Extraction in Dense Areas using Super Resolution and
Frame Field Learning

1st Vuong Nguyen  Department of Computer Science
University of Houston
Houston, Texas, USA
<EMAIL_ADDRESS>2nd Trong-Anh Ho  Skymap Global
Hanoi, Vietnam
<EMAIL_ADDRESS>3rd Duc-Anh Vu  Nanyang Technological University
Singapore
<EMAIL_ADDRESS>4th Nguyen Thi Ngoc Anh  School of Applied Mathematics
and Informatics
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>5th Tran Ngoc Thang∗ School of Applied
Mathematics and Informatics
Hanoi University of Science and Technology
Hanoi, Vietnam
<EMAIL_ADDRESS>
###### Abstract

Despite notable results on standard aerial datasets, current state-of-the-arts
fail to produce accurate building footprints in dense areas due to challenging
properties posed by these areas and limited data availability. In this paper,
we propose a framework to address such issues in polygonal building
extraction. First, super resolution is employed to enhance the spatial
resolution of aerial image, allowing for finer details to be captured. This
enhanced imagery serves as input to a multitask learning module, which
consists of a segmentation head and a frame field learning head to effectively
handle the irregular building structures. Our model is supervised by adaptive
loss weighting, enabling extraction of sharp edges and fine-grained polygons
which is difficult due to overlapping buildings and low data quality.
Extensive experiments on a slum area in India that mimics a dense area
demonstrate that our proposed approach significantly outperforms the current
state-of-the-art methods by a large margin.

###### Index Terms:

Building footprint extraction, Super resolution, Frame field learning, Aerial
imagery, Urban planning.

##  I Introduction

Extracting building footprints from aerial or satellite images is an important
task in remote sensing and computer vision. It is used for urban planning,
disaster management, and environmental monitoring. Thanks to the advances of
deep learning techniques, promising results have been shown in delineating
building footprints from aerial imagery. The initial approaches primarily
revolved around the prediction of vectorized building instances, which is
often coupled with post-processing methods. Zhao et al. [1] introduced a
multi-step boundary regularization technique using the paradigm of Mask-RCNN,
while Li et al. [2] refined polygonal partitions using U-Net based
architectures. A Generative Adversarial Network (GAN) based method was
proposed to enhance the quality of the output segmentation without depending
excessively on segmentation results [3]. Later methods involved the direct
prediction of building polygon vertices using Convolutional Neural Network [4]
or Graph Convolutional Networks [5]. Recently, frame field learning has been
proposed [6] to capture local orientation and directional attributes from
building structures.

While comparable results have been reported on current benchmarks [7, 8] that
cover regular areas, little work has been studied on extraction of buildings
in dense areas (e.g. rural slum areas). These areas contain densely packed and
irregularly arranged buildings, posing difficulties for existing methods to
effectively extract building footprints.

In this work, we leverage our curated dataset covering a slum area in India,
and use it as our benchmark. We propose a novel framework that integrates
super resolution, frame field learning, and polygonization techniques, which
enhances accuracy and robustness for building outlines extraction in these
complex settings. First, super resolution (SR) is employed to capture finer
details from complex building structures present in dense areas. The multitask
learning module then takes SR imagery as input to produce a segmentation mask
and a frame field, which complementarily optimize each other to effectively
handle the irregular building outlines. Finally, a polygonization module is
utilized to refine the contours, resulting in regular footprints with sharp
edges.

In summary, we propose to enhance the extraction of building footprints in
highly dense areas by: (1) incorporating advanced spatial resolution
enhancement for aerial imagery (2) combining segmentation with frame field in
a multitask learning module and (3) performing polygonization to address the
challenges in extracting building footprints in dense areas. Experiments
carried out on our benchmark show superiority of our framework over current
state-of-the-art methods.

##  II Related Works

Figure 1: Visualization of a part of the aerial image used in this paper. A
slum area in Mumbai, India containing around $15,000$ building objects is
covered. The study area is characterized with highly dense and irregular
building structure.

###  II-A Building Footprint Segmentation

Building footprint segmentation is the process of identifying and delineating
the outlines of buildings from satellite or aerial imagery. Early approaches
emphasizes the use of algorithms such as graphs and regions [9], [10]. Recent
years have seen the dominance of deep learning-based methods in building
footprint extraction task [11, 12, 13]. Li et al. [2] proposed a polygonal
partition refinement method for vectorizing the output probability maps of a
U-Net based model. Besides, DeepLabv3+ [14] leverages dilated convolutions and
a decoder module to achieve high-resolution predictions. A boundary
regularization approach consisting of multiple steps to regulate the predicted
building instances generated by Mask-RCNN is introduced in [1], resulting in
simplified building polygons. However, the data dependency problem of the
above methods significantly degrades their performance in dense areas which
poses severe difficulties for accurate extraction.

###  II-B Super Resolution

Super resolution method is a technique of recovering a high-resolution image
from a low-resolution image. Dong et al. [15] introduced SRCNN which surpassed
the performance of previous studies. VDSR [16] and IRCNN [17] proposed to
increase the network depth by incorporating additional convolutional layers
using residual learning. Real-ESRGAN [18], which incorporates GAN technique to
effectively upscale spatial resolution in aerial imagery, is utilized as one
of the key components of our framework.

###  II-C Frame Field Learning

Frame field learning refers to the process of incorporating a frame field
output into a fully-convolutional network, which improves segmentation
performance by enhancing sharp corners and provides valuable information for
vectorization. It is early used by [19] to devise a straightforward
polygonization. In addition, frame field learning is applied in solving road
segmentation tasks [20]. Girard et al. [6] leverages frame field learning to
provide structural information for polygonal building segmentation. However,
achieving good generalization across dense areas in real-world scenarios
remains a challenge for these methods, where irregular building structures
severely undermine the contribution of frame field to improve segmentation
results.

##  III Study Area and Dataset

In this paper, we cope with highly dense areas, which pose several challenges
to the extraction of building contours due to their properties. First, dense
areas are characterized by irregular and ambiguous building patterns along
with variations in building types and shapes, which makes defining consistent
criteria for extraction not easy. Second, high density leads to overlapping
structures and shared walls, making it difficult to distinguish individual
footprints. Last but not least, data sources covering these areas are limited
or of low quality.

In this work, we leveraged an aerial image that covers a slum area of
$1,1km^{2}$ in Mumbai, India. The image has a spatial resolution of $0.3m$ as
shown in Figure 1. We first converted the image into three-band, RGB image for
further processing. A total of approximately $15,000$ building objects are
present in the whole study area. We manually annotated the associated building
polygons which are the ground truth contours represented by the sequence of
pixel coordinates that form the outline of the buildings. We then performed
Super Resolution (SR) on the aerial image with details discussed in the
following section. A dataset was then constructed by cropping the SR aerial
image into $1,900$ images of size $256\times 256$. We split the dataset into
training, validation and test set with a proportion of $60\%,20\%$ and $20\%$
respectively. In this study, no data augmentation method was applied.

##  IV Method

Figure 2: Overview of our proposed framework.

The overview of our proposed framework is illustrated in Figure 2. First,
Super Resolution is performed on the original aerial image. Then, given an
image $I$ of size $3\times 256\times 256$ as input, the U-Net-like Multi-task
Learning module outputs a segmentation mask and a frame field. Segmentation
masks are then aligned with frame field in Frame Field Alignment module. A
Polygonization module is then employed to handle non-trivial topological
property of building boundaries.

###  IV-A Super Resolution

Previous frame-field-learning-based works [21, 22] have achieved notable
results on such datasets covering standard study areas with sparse and regular
building structure. However, these methods fail to extract irregular and dense
buildings in dense areas due to low quality and limited data sources. In this
work, we propose to apply Super Resolution to our original aerial image.
Enhanced spatial resolution helps to mitigate the challenges posed by
properties of dense areas by boosting visual sharpness and generating more
detailed representations of the buildings [23].

We employ Real-ESRGAN [18] to enhance spatial resolution of the original
aerial image. Real-ESRGAN is a generative model that mainly consists of a
generator $\mathcal{G}$ and a discriminator $\mathcal{D}$, both are trained in
an adversarial manner. Generator $\mathcal{G}$ reconstructs the corresponding
super resolution (SR) image (denoted as $I_{SR}$) from the given low
resolution (LR) image (denoted as $I_{LR}$), i.e.
$I_{SR}=\mathcal{G}(I_{LR})$. In this work, $I_{LR}$ is the original aerial
image (Figure 1) with a spatial resolution of $0.3m$, and $I_{SR}$ is upscaled
to a spatial resolution of $0.15m$. Building edges, corners in aerial images
of dense areas are discriminative cues that can aid in accurately
distinguishing buildings from other objects in the scene. The generator
$\mathcal{G}$ restores these texture details by using several Residual-in-
Residual Dense Blocks (RRDB), each combines multi-level residual network and
dense connection with batch normalization removed. Beside, $\mathcal{G}$ also
employs pixel-unshuffle to reduce the large spatial size while increasing the
channel size, leading to more precise features for footprints extraction.

On the other hand, discriminator $\mathcal{D}$ attempts to distinguish between
real high resolution image and generated SR image generated by $\mathcal{G}$.
The improved U-Net discriminator $\mathcal{D}$ with skip connections brings a
greater discriminative power for complex aerial image of dense areas.
Moreover, discriminator $\mathcal{D}$ provides more detailed feedback to the
generator by outputting pixel-wise realness values. Hence, the generator is
capable of generating sharper local features, which facilitates the
recognition of small and irregular buildings in dense areas.

(a) Sample input data

(b) Enhanced SR data

Figure 3: SR data enhances details of highly dense building complex for more
accurate footprint extraction.

As shown in Figure 3, by enhancing the resolution and quality of the aerial
images, we have enabled the subsequent extraction stage to achieve improved
accuracy in identifying and delineating building footprints. This, in turn,
contributes to better understanding and management of slum areas, facilitating
effective urban planning and development initiatives.

###  IV-B Multitask Learning

####  IV-B1 Multitask Learning Module

Following [21], besides outputting segmentation mask, we leverage Frame Field
Learning to accurately extract polygonal edges and sharp corners without the
need for high-cost post-processing techniques. In a frame field plane, each
point is assigned a set of four vectors $\\{i,-i,j,-j\\}$. Two complex numbers
$i,j\in\mathbb{C}$ define the directionality at each point, thus, help capture
missing shape information at corners. The pair of directions of each frame can
be easily recovered given a pair $(k_{0},k_{1})$ which are the encoded
coefficients of:

$f(z)=(z^{2}-i^{2})(z^{2}-j^{2})=z^{4}+k_{1}z^{2}+k_{0}.$ (1)

A smooth frame field is obtained by learning a pair $(k_{0},k_{1})$ in 1 at
each pixel as explored in [24]. Note that the field at polygon corners aligns
to both tangent directions to ensure the frame field not collapse to a line
field.

As shown in Figure 2, the Multitask Learning module leverages a U-Net-like
network to simultaneously learn the segmentation masks and a frame field. The
network is constructed with two internal branches, both take an
$F$-dimensional feature map $f\in\mathbb{R}^{F\times 256\times 256}$ as input.
One branch produces the segmentation map $f^{seg}\in\mathbb{R}^{2\times
256\times 256}$ which is a binary classification map with two labels: building
interiors (denoted as $f^{int}$) and building boundaries (denoted as
$f^{edge}$). The other branch combines the segmentation masks and output
feature maps $[f,f^{edge}]\in\mathbb{R}^{(F+2)\times 256\times 256}$ as input
and outputs the frame field with
$\hat{k}_{0},\hat{k}_{1}\in\mathbb{C}^{256\times 256}$.

Binary cross-entropy loss $\mathcal{L}_{BCE}$ is leveraged jointly with Dice
loss $\mathcal{L}_{Dice}$ [25] to drive the segmentation head. Given ground
truth $y^{int}$ and $y^{edge}$, interior mask $f^{int}$ is corrected by
$\mathcal{L}_{int}$ formulated as:

$\mathcal{L}_{int}=c\mathcal{L}_{BCE}(f^{int},y^{int})+(1-c)\mathcal{L}_{Dice}(f^{int},y^{int})$
(2)

and corresponding edge mask $f^{edge}$ is corrected by $\mathcal{L}_{edge}$
analogous to 2. In this work, $c$ was set to $0.25$. On the other hand, given
the ground truth angle $\theta_{\eta}\in[0,\pi)$ of the unsigned tangent
vector of the polygon contour, frame field is forced to by aligned to tangent
directions $\hat{k}_{0},\hat{k}_{1}\in\mathbb{C}^{H\times W}$ by the alignment
loss:

$\mathcal{L}_{align}=\frac{1}{256^{2}}\sum_{x\in
I}y^{edge}(x)|f\left(e^{i\theta_{\eta}};\hat{k}_{0}(x),\hat{k}_{1}(x)\right)|^{2}$
(3)

while the orthogonal alignment loss $\mathcal{L}_{align90}$ analogous to 3
prevents line-collapsing of frame field by aligning it to
$\eta-\frac{\pi}{2}$.

####  IV-B2 Frame Field Alignment Module

Given a building, initially, a contour is derived from the segmentation
output. Then, the contour is iteratively refined by aligning it with the frame
field, ensuring sharp corners with accurate directions. Specifically, first,
directionality of frame field is corrected by smoothening
$\hat{k}_{0}(x),\hat{k}_{1}(x)$ by $\mathcal{L}_{smooth}$ given as:

$\mathcal{L}_{smooth}=\frac{1}{256^{2}}\sum_{x\in
I}\|\triangledown\hat{k}_{0}(x)\|^{2}+\|\triangledown\hat{k}_{1}(x)|^{2}\|$
(4)

Output coupling losses are used to enhance consistency between segmentation
and frame field outputs. Spatial gradients of the $f^{int}$ and $f^{edge}$ are
aligned with the frame field by interior alignment loss
$\mathcal{L}_{int\,align}$ and edge alignment loss $\mathcal{L}_{edge\,align}$
which are analogous to 3. As buildings in dense areas are closely packed
together making them likely to share common walls, $\mathcal{L}_{int\,edge}$
is built to improve the model’s ability to detect adjoining buildings in the
edge mask:

$\displaystyle\mathcal{L}_{int\,edge}$ $\displaystyle=\frac{1}{HW}\sum_{x\in
I}\max\left(1-f^{int}(x),\|\triangledown f^{int}(x)\|_{2}\right)$ (5)
$\displaystyle\cdot|\|\triangledown f^{int}(x)\|_{2}-f^{edge}(x)|$

####  IV-B3 Adaptive Loss Weighting

In this work, different from [21] which assigns equal weighting for every loss
in the linear combination of final loss, we propose to further improve
multitask learning by amplifying the contribution of each loss to its
corresponding task [26]. Specifically, noise parameters $\sigma$ is leveraged
as variable in minimizing the final loss:

$\displaystyle\mathcal{L}=$
$\displaystyle\frac{1}{2\sigma^{2}}(\mathcal{L}_{smooth}+\mathcal{L}_{int\,align}+\mathcal{L}_{edge\,align}+\mathcal{L}_{int\,edge})+$
$\displaystyle\frac{1}{\sigma}\mathcal{L}_{edge}+\frac{1}{\sigma^{2}}(\mathcal{L}_{int}+\mathcal{L}_{align}+\mathcal{L}_{align90})+\log\sigma$

i.e. each regularizer loss is weighted $\frac{1}{2\sigma^{2}}$ while each
classification loss is weighted $\frac{1}{\sigma^{2}}$, except for
$\mathcal{L}_{edge}$ which accounts for the largest weight to resolve
difficulty in extracting sharp edges caused by tiny and overlapping buildings.
Additional regularizer $\log\sigma$ is added to prevent
$\sigma\rightarrow\infty$.

###  IV-C Polygonization

The building contours output by Frame Field Learning are then post-processed
to smoothen and simplify the corners. Leveraging the interior map and frame
field, a polygonization method is designed to first extract the initial
contour and then optimize the contour by an Active Contour Model (ACM) [27].
This helps better align the edges to the frame field and also produce a more
regular building shape. Polygons with low probabilities are removed, leaving
final polygons.

Specifically, building boundary $f^{edge}$ is first skeletonized, creating a
skeleton graph, i.e. the graph of connected pixels of the skeleton image.
Using gradient descend, Active Skeleton Model is conducted in which ACM
performs optimization stage which iteratively moves the points on the skeleton
graph toward optimal positions. Corner vertices are then detected using the
direction information from the frame field. Then, polylines are obtained by
splitting the contour at corner vertices. Polylines are then further
simplified, outputting a polygon with a more regular shape.

##  V Experimental setup

###  V-A Evaluation Metrics

In this work, for each building object, we first computed $IoU$ value which
measures the intersection area between predicted segmentation masks and ground
truths. F-1 score, Average Precision (AP), and Average Recall (AR) are
measured under the $IoU$ threshold ranging from $0.50$ to $0.95$ with a step
of $0.05$. We report the results under the $IoU$ thresholds of $0.50$ and
$0.75$, giving $F1_{50}$, $F1_{75}$, $AP_{50}$, $AP_{75}$, and $AR_{50}$,
$AR_{75}$ respectively. We also report Mean Average Precision (denoted as
$mAP$) and Mean Average Recall (denoted as $mAR$), which provide a
comprehensive understanding of the model’s accuracy in accurately delineating
building footprints.

TABLE I: Comparison in quantitative results on our dataset. SR denotes super resolution data, while ORG denotes original data. Overall, our framework outperforms current state-of-the-art by a large margin especially with high $IoU$ thresholds.  Method | $F1_{50}$ | $F1_{75}$ | $mAP$ | $mAR$ | $AP_{50}$ | $AP_{75}$ | $AR_{50}$ | $AR_{75}$
---|---|---|---|---|---|---|---|---
Mask-RCNN[28]-U2Net[29] \+ SR | $45.2$ | $22.7$ | $32.1$ | $18.5$ | $\mathbf{61.9}$ | $31.2$ | $35.6$ | $17.9$
Multitask Learning [21] \+ ORG | $41.5$ | $27.7$ | $26.7$ | $24.2$ | $43.7$ | $29.1$ | $39.6$ | $26.4$
Multitask Learning + SR (Ours) | $\mathbf{59.1}$ | $\mathbf{39.8}$ | $\mathbf{34.5}$ | $\mathbf{38.9}$ | $55.8$ | $\mathbf{37.5}$ | $\mathbf{62.9}$ | $\mathbf{42.3}$

(a) MaskRCNN-U2Net + SR

(b) Multitask Learning + ORG

(c) Our proposed framework

(d) Ground truth

Figure 4: The qualitative comparison in visualized outputs among Mask-
RCNN-U2Net on super resolution data (SR), Frame Field Learning on original
data (ORG) and our proposed framework.

###  V-B Implementation Details

For Super Resolution, we leveraged RealESR-GAN [18], where we pretrained it
with $6120$ aerial images with a scale list of $[1,0.75,0.5,1/3]$. The images
containing mostly building objects were gathered from several datasets which
cover a broad range of spatial resolution (from $0.04m$ to $0.5m$) and
multiple areas to improve generalization ability. Training Real-ESRGAN is
conducted with $100,000$ iterations. The pretrained model is then used to
perform inference on our aerial image (Figure 1). The output SR image is then
cropped to create input images for the subsequent Multitask Learning module.

For Multitask Learning module backbone, we used a DeepLabV3 [14] model called
DeepLab101, in which a ResNet-101 encoder [30] is utilized. Instead of random
initialization, we employed pre-trained model from [21]. Following the
original U-Net [31], distance weighting for the cross-entropy loss is also
used. We trained our model with $200$ epochs using Adam optimizer with a batch
size of $16$, an initial learning rate of $0.001$ and a decay rate of $0.9$.

##  VI Results and Discussion

Quantitative results on our dataset (Figure 1) are reported in Table I, while
qualitative extraction results are visualized in Figure 4. We compare our
proposed framework with two state-of-the-arts (SOTAs) including the
segmentation-based method Mask-RCNN [28] with backbone U2-Net [29] trained on
Super Resolution (SR) data and the multitask-learning-based method proposed in
[21] trained on original data. Overall, our proposed framework outperforms
previous SOTAs by a significant margin in all evaluation metrics.

####  VI-1 Comparison with Semantic Segmentation Model on Super Resolution
data

From Table I. Our method outperforms Mask-RCNN-U2Net in $mAR$ by a large
margin of $20.4\%$. The performance of our method is notably more remarkable
than Mask-RCNN-U2Net in both Average Precision and Average Recall on high
$IoU$ thresholds, i.e. $17.1\%$ higher in $F1_{75}$, $27.3\%$ higher in
$AP_{75}$, and $24.3\%$ higher in $AR_{75}$. Significant improvement on high
IoU threshold indicates more precise extraction results produced by our
proposed framework compared to the segmentation-based SOTA method.

The qualitative comparison on extraction results visualized in Figure 4(a) and
4(c) demonstrate that our proposed framework is superior to Mask-RCNN-U2Net in
many aspects. First, more complete building outlines are obtained. Second,
tiny and neighboring buildings from a highly dense region are accurately
extracted, compared to false sparse building outlines produced by Mask-
RCNN-U2Net. In Figure 5, we show a failure case of Mask-RCNN-U2Net and how our
proposed framework effectively handled the tiny and overlapping buildings in
dense areas. The building boundaries are also well-refined using our
framework, shown by more regular polygons with thinner, smoother edges and
sharper corners. The reason is Mask-RCNN-U2Net solely relies on the ability of
model to detect bounding boxes and segment objects from bounding boxes, which
is severely affected by overlapping masks. On the other hand, by combining
frame field and segmentation, our proposed framework simultaneously obtain
masks and contours, followed by a frame-field-based polygonization stage to
better align polygon edges and vertices.

Figure 5: A failure case of Mask-RCNN-U2Net (left) compared to our proposed
framework (right) on original data.

####  VI-2 Effectiveness of Super Resolution and Adaptive Loss Weighting

To demonstrate that the multitask learning is improved by utilizing Super
Resolution (SR), we also compare our proposed framework with the method
proposed in [21] trained on original data, while we trained our method on SR
data with adaptive loss weighting. From Table I, it can be seen that our
proposed framework is superior in all evaluation metrics, i.e. $7.8\%$ higher
in $mAP$ and $14.7\%$ higher in $mAR$, showing that the proposed Super
Resolution stage significantly improves the ability of model to learn accurate
frame field and regular polygons, while building edges are well captured by
adaptive loss weighting.

In terms of qualitative extraction results, it can be seen from Figure 4(b)
and 4(c) that low image quality remarkably degrades the performance of the
framework in [21], which suffers from severe confusion caused by blur edges
and high density. Using SR, the segmentation-based Mask-RCNN-U2Net is able to
produce more complete building outlines than the multitask-learning-based
method in [21]. The failure case visualized in Figure 6 demonstrates that
enhanced spatial resolution of the aerial image enables our method to
recognize ambiguous contours even for tiny buildings. Edges are further
sharpened, while invalid vertices are filtered out, which makes our model
robust to poor segmentation results.

Figure 6: A failure case of the method proposed in [21] trained on original
data (left). Our method trained on super resolution data shows superiority and
robustness in extraction (right).

##  VII Conclusion

In this paper, we have addressed the challenges encountered in extracting
building outlines from aerial images of dense areas by employing the
techniques of Super Resolution (SR) and Multitask Learning. Extensive
experiments demonstrate that utilizing SR to enhance the spatial resolution of
aerial image improves the accuracy of the subsequent segmentation process.
Moreover, the integration of frame field learning facilitated the preservation
of local textures and fine details crucial for the extraction of sharper
edges, which surpasses existing techniques in terms of accuracy. The superior
performance achieved by our proposed approach highlights its potential and
future directions for practical applications in urban planning, which aim at
improving living conditions in slum areas.

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