# Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar
(SAR) Datacubes

Vanessa Boehm
University of California Berkeley
United States
Wei Ji Leong ∗
The Ohio State University
United States
Ragini Bal Mahesh ∗
German Aerospace Center DLR
Germany
Ioannis Prapas ∗
University of Valencia, Spain
National Observatory of Athens, Greece
Edoardo Nemni
United Nations Satellite Centre
Switzerland
Freddie Kalaitzis
University of Oxford
United Kingdom

Siddha Ganju
NVIDIA
United States
Raul Ramos-Pollan
Universidad de Antioquia
Colombia
Authors contributed equally.

###### Abstract

With climate change predicted to increase the likelihood of landslide events,
there is a growing need for rapid landslide detection technologies that help
inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing
technique that can provide measurements of affected areas independent of
weather or lighting conditions. Usage of SAR, however, is hindered by domain
knowledge that is necessary for the pre-processing steps and its
interpretation requires expert knowledge. We provide simplified, pre-
processed, machine-learning ready SAR datacubes for four globally located
landslide events obtained from several Sentinel-1 satellite passes before and
after a landslide triggering event together with segmentation maps of the
landslides. From this dataset, using the Hokkaido, Japan datacube, we study
the feasibility of SAR-based landslide detection with supervised deep learning
(DL). Our results demonstrate that DL models can be used to detect landslides
from SAR data, achieving an Area under the Precision-Recall curve exceeding
0.7. We find that additional satellite visits enhance detection performance,
but that early detection is possible when SAR data is combined with terrain
information from a digital elevation model. This can be especially useful for
time-critical emergency interventions. Code is made publicly available at
https://github.com/iprapas/landslide-sar-unet.

##  1 Introduction

According to the United Nations Office for Disaster Risk Reduction, landslides
have affected 4.8 million people and caused 18,414 deaths between 1998-2017
[1]. Rising temperatures and climate change are projected to worsen this
situation [2, 3] with the increase of sustained droughts and intense rainfall
events [4, 5, 6]. Given these predictions, there is a growing need for timely
and accurate landslide assessment methods that can inform decision makers and
emergency responders. In the aftermath of a landslide, optical satellite
imagery is commonly used to map the extent of the event. The fact that optical
data is is often hindered by clouds and limited to day time observations,
motivates the study of weather independent Synthetic Aperture Radar (SAR) as a
monitoring technique.

DL algorithms for computer vision can be used for SAR analysis if SAR data is
represented as images (or georeferenced data arrays) [7]. This has been
exploited for multiple use cases such as the detection of volcanic activity
[8], floods [9], and landslides[10]. The landslide change detection with DL
can be performed in two ways: object-level [11] and pixel-level detection
[10]. As per the authors’ best knowledge [10, 11] are one of the first studies
in landslide mapping using DL and SAR images where bi- and tri-temporal images
have been used. Usage of multi-temporal SAR polarization modes has been
demonstrated with traditional approaches, such as thresholding [12], similar
conclusion was reported in a DL study [13]. To achieve good accuracy, these
methods require longer time series of SAR data (up to months after the event),
which are unavailable in an emergency scenario.

The development of DL algorithms for SAR analysis, however, is still hindered
by its complex characteristics and pre-procesisng requirements. To help
overcome this barrier, we publicly release curated DL-ready datasets [14] that
includes SAR intensity and interferometry data before and after disaster
events that triggered landslides. Using part of this dataset, we further
develop a supervised DL algorithm for landslide detection and demonstrate the
feasibility of rapid detection using only one satellite pass, if SAR data is
combined with terrain information.

##  2 Data

We produce analysis-ready datacubes [14] with multiple layers of SAR time-
series data and auxiliary geographic information for multiple landslide
events. To this end, we develop a data pipeline that processes SAR intensity
and interferometric products from multiple sources and compile them together
with a Digital Elevation Model (DEM) and DEM-derived products into a format
that can be fed into DL models (see Fig. 1).

Figure 1:  Sample data used to train the DL model. In each of panels 1-8, we
show one channel of the input data, in the last panel we show the target
landslide mask. 1-4: Polarized Synthetic Aperture Radar intensity images (VV
and VH) from before and after the landslide trigger event; 4-8: Digital
Elevation Model (DEM, elevation) and DEM-derived products (aspect, slope,
curvature).

In this study, we focus our machine learning experiments on the datacube
created for earthquake-triggered landslides over Hokkaidō, Japan in 2017 (Fig
2). Additional datacubes are available for landslide events over Mt Talakmau,
Indonesia (2022 earthquake), Kaikōura, New Zealand (2016 earthquake) and
Puerto Rico (2017 Hurricane) (see Appendix A for more details on datacube
creation).

Figure 2:  Location map of the Hokkaido, Japan study area (a, blue box) and
data layers covering spatial area of about 236 km2 (4398x5382 pixels). Two VV-
channel SAR images before (b) and after (c) the earthquake are shown, together
with the landslides labels (d) and SRTM DEM (e). The actual datacube covers a
temporal period from 2018/07 to 2019/11, with SAR images in ascending mode
only, consisting of 18 dual polarized SAR (VV and VH) timesteps. Although not
used in this study, the datacube also contains InSAR channels (coherence and
interferometry) between 16 pairs of consecutive acquisitions.

##  3 Methodology

###  3.1 Experimental setup

The machine learning pipeline is set up to solve a segmentation task (Fig. 3).
We divide the Hokkaido datacube into data chips of size 128x128 pixels (each
pixel is 10m$\times 10m$ in spatial resolution). Since the dataset is
imbalanced, we reduce negative examples by keeping only data chips with one or
more landslide pixels, similar to Nava et al. [11]. These chips are split into
training, and test sets with 216, and 61 chips respectively. Model performance
is measured using the Area Under the Precision Recall Curve (AUPRC), as it is
appropriate for imbalanced datasets. About 9% of pixels in the dataset are
labeled as landslides, which makes the baseline AUPRC 0.09.

A U-Net [15] model with a ResNet-50 encoder (48,982,754 trainable parameters)
and varying number of input channels is trained for 100 epochs using an Adam
optimizer (weight decay=0.0001) and a
ReduceLROnPlateau111https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
learning rate schedule (factor=0.1, patience=10, threshold=0.0001) with an
initial learning rate of 0.01. We use a standard cross-entropy loss to compare
the model outputs to the landslide segmentation masks. VV and VH channels are
log-transformed and standardized before serving as input.

Figure 3:  Machine Learning pipeline from the Synthetic Aperture Radar (SAR)
datacube (left) to landslide map predictions (right). SAR time-series data and
Digital Elevation Model (DEM) products are first extracted into 128x128 chips.
Next, batches of chips are fed via a DataLoader into the U-Net model with a
ResNet-50 encoder. The trained U-Net model is then used to do inference and
produce landslide segmentation maps.

We test different combinations of inputs channels (shown in Figure 1) to
identify the most informative features and their combinations for the
landslide detection task. Along with SAR polarimetry data before and after the
event we use as input DEM-derived features (elevation, slope, aspect and
curvature). When using multiple timesteps of SAR inputs, we take the pixel-
wise mean value. This means that using a combination of VV/VH polarimetry
bands results in 4 input channels (VV before, VH before, VV before, VV after).
We test how many timesteps/satellite passes before and after the event need to
be aggregated to obtain reliable landslide segmentation predictions.

We run experiments with the following inputs:

  * •

SAR (VV): Only VV band before and after the earthquake (total 2 input
channels).

  * •

SAR (VH): Only VH band before and after the earthquake (total 2 input
channels).

  * •

SAR (VV,VH): Only SAR bands (VV and VH) are used before and after the
earthquake (total 4 input channels).

  * •

SAR+DEM: SAR bands (VV and VH) before and after the earthquake, plus DEM-
derived data (elevation, slope, aspect, curvature) are used (total 8 input
channels).

For each input configuration, we train using 1, 2, 3 or 4 time steps (average
pixel-wise into 1 channel) before and after the event. Notice that regardless
the number of time steps we use, the number of SAR channels remains constant
as we take the pixel-wise mean value across all time steps.

All models were trained on 1 NVIDIA Tesla V100 GPUs on the Google Cloud
Platform. Training one model requires approximately 0.25 GPU hours.

##  4 Results

Results of our input ablation study are shown in Fig 4. We find that
increasing the number of time steps increases the performance of the model for
all input configurations. VV is more informative than VH for landslide
detection in Hokkaido, and the combination of VV and VH achieves the best
results. Combining the SAR inputs with DEM-derived products, we can achieve
better AUPRC when only 1 or 2 satellite overpasses are available. This shows
that the addition of DEM-derived data can facilitate early detection in
crisis-management scenarios. There is no evident gain from adding DEM-derived
channels when more than 2 SAR timesteps are available.

Figure 4:  Test Area under the Precision-Recall Curve (AUPRC) metrics for
different experiments. The x-axis shows the number of SAR timesteps used,
while the y-axis shows the AUPRC metric. The four lines represent experiments
using various combinations of Synthetic Aperture Radar (SAR) and Digital
Elevation Model (DEM) inputs, from bottom to top (red: VH channel only; green:
VV channel only; orange: SAR VV+VH channels + DEM; blue: SAR VV+VH channels
with no DEM).

It is important to note that a random model achieves a baseline AUPRC of 0.09
and a model trained with just the DEM and DEM-derived features achieves a
maximum AUPRC of 0.17. This demonstrates the value added by SAR data and the
complementarity of SAR and DEM input channels.

Figure 5:  Examples of predicted landslide maps (top row) versus groundtruth
target maps (bottom row) from the test set. Background (no landslide) areas in
purple, landslide affected areas in yellow.

##  5 Conclusion

Rapid and automated disaster assessment is becoming more crucial than ever as
climate change increases both the severity and frequency of weather-induced
disasters. Here, we have demonstrated the feasibility of using deep learning
to detect landslides from complex Synthetic Aperture Radar data and measure
the trade offs of time series data for rapid response scenarios. Both the
scalability of deep learning methods and the fact that SAR measurements are
not obstructed by weather or night time conditions have the potential to
significantly shorten assessment times.

To further foster research in this direction, and help bridge the barriers for
computer vision specialists to work with SAR data, we make public the
analysis-ready data cubes for landslide detection [14].

Future work for this use case could include measuring the contribution on SAR
interferometry, self-supervised pretraining on a subset of the datacube and
understanding how this model could be applied to other regions of the world
with different terrain, vegetation coverage and geological characteristics.

## Acknowledgments and Disclosure of Funding

This work has been enabled by the Frontier Development Lab Program (FDL). FDL
is a collaboration between SETI Institute and Trillium Technologies Inc., in
partnership with Department of Energy (DOE) , National Aeronautics and Space
Administration (NASA), and U.S. Geological Survey (USGS). The material is
based upon work supported by NASA under award No(s) NNX14AT27A. Any opinions,
findings, and conclusions or recommendations expressed in this material are
those of the authors and do not necessarily reflect the views of the National
Aeronautics and Space Administration.

We thank Aaron Pina, our contact from NASA who helped shape this research. He
also facilitated communication with experts from NASA and ASF, namely Gerald
Bawden (NASA), Eric Fielding (NASA), Alex Handwerger (NASA), Erika Podest
(NASA), Franz Meyer (ASF), Forrest Williams (ASF). We also thank our FDL
reviewers Brad Neuberg (Planet), Ronny Hänsch (DLR), Erika Podest (NASA),
Ioannis Papoutsis (National Observatory of Athens) who provided great feedback
on specificities of ML for SAR.

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##  Appendix A Datacube creation

Earth Observation datacubes are analysis-ready data [16, 17], an intermediate
product flexible enough to be used by both remote sensing scientists looking
at time-series data on a geographic region, and by machine learning experts
who can ingest the data as tensors into neural network models.

Multiple streams of the data processing feed into the datacubes. Given a
geographical area of interest (AOI) and a time range, we perform a
spatiotemporal query to several SAR satellite data providers that return a
list of raw Sentinel-1 granules matching the search criteria. SAR data is
composed of amplitude and phase information and we handle them separately in
our data pipeline.

Figure 6:  Datacube containing Synthetic Aperture Radar (SAR) data and other
geographic layers.

For amplitude-based SAR products, we directly obtain Sentinel-1
Radiometrically Terrain Corrected (RTC) data hosted on Microsoft Planetary
Computer222https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc
which has been processed from Ground Range Detected (GRD) data. The RTC data
is stacked both in the polarimetric channels and time to produce an xarray
dataset [18] with dimensions (time, y, x), and two polarimetric directions, VV
and VH.

For the phase-based SAR product, we download the Sentinel-1 Single-Look
Complex (SLC) product from the Alaska Satellite Facility (ASF). SLC data is
further processed by using the phase information of two consecutive time steps
(timepair, with a start and end date) to create interferogram and coherence
maps with interferometry. SAR interferometry is used to measure the surface
deformation with differences in phase of two SAR images over a region of
interest and coherence is an indicator for agreement between the two phases.
For these processing step, we used the InSAR Scientific Computing Environment
(ISCE, version 2) software package [19, 20] developed at the NASA Jet
Propulsion Laboratory, California Institute of Technology. The topsApp program
of the software is used on the interferometric wide (IW) swath mode SLC
products with azimuth looks = 1 and range looks = 5 for processing of the
interferograms. This includes steps such as computing the baseline using orbit
files, co-registration with enhanced spectral diversity, calculating
interferograms, geocoding. Note that no phase unwrapping is performed here.
Thus phase-derived products have dimensions (timepair, y, x) and the data
variables are ‘interferogram’ and ‘coherence’.

Besides the Sentinel-1 SAR data, we include two additional geographical
datasets in our data pipeline. The Shuttle Radar Topography Mission (SRTM) DEM
[21] is downloaded, and processed to derive slope and aspect data layers.
Landslide vector data for the multiple regions is obtained from multiple
sources [22, 23, 24, 25]. The vector files from these sources are rasterized
to the same pixel resolution and spatial extent as the other raster SAR data.

Finally, we assemble all the SAR and non-SAR products into a single datacube.
The datacube itself is initialized from the RTC data with dimensions
(timestep, y, x) and data variables VV and VH. Next, the DEM and landslide
labels are appended as new data variables along the y and x dimensions. Then,
the phase-derived products are appended, not just along the y and x
dimensions, but also along a new ‘timepair’ dimension containing the first and
second dates of the Sentinel-1 scenes used to create the interferogram. All of
these appended products are geographically reprojected and aligned using
rioxarray [26] if necessary to match the RTC template. The resulting xarray
Dataset [18] has dimensions (timepair, timestep, y, x) and is stored in a
cloud-friendly Zarr format [27]. The datasets can be found in Zenodo [14].