2 research outputs found
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
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.Comment: Accepted in the NeurIPS 2022 workshop on Tackling Climate Change with
Machine Learning. Authors Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh,
Ioannis Prapas contributed equally as researchers for the Frontier
Development Lab (FDL) 202