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Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
Vanessa Boehm · Wei Ji Leong · Ragini Bal Mahesh · Ioannis Prapas · Siddha Ganju · Freddie Kalaitzis · Edoardo Nemni · Raul Ramos-Pollán

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in Tackling Climate Change with Machine Learning »
Event URL: https://www.climatechange.ai/papers/neurips2022/50 »

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 from 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.

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Author Information

Vanessa Boehm (UC Berkeley)
Wei Ji Leong (The Ohio State University)
Ragini Bal Mahesh (German Aerospace Center DLR)
Ioannis Prapas (University of Valencia, National Observatory of Athens)
Siddha Ganju (Nvidia)
Freddie Kalaitzis (University of Oxford)
Freddie Kalaitzis

Freddie is a Senior Research Fellow at the Dept. of Computer Science, University of Oxford, investigating topics mainly in AI for Earth Observation. He is the principal investigator of OpenSR, a €1M government contract with ESA, to increase the safety of Super-Resolution technology for the Sentinel-2 archive. He is also an independent consultant, involved in projects where he leads teams in the Frontier Development Lab (FDL), a private-public partnership between NASA, SETI, and Trillium Technologies. His recent FDL projects were funded by NASA SMD to investigate the use of SAR imagery for disaster detection, and by the USGS to develop near-real-time water stream mapping from daily PlanetScope imagery. His most recent work is a survey on the State of AI for Earth Observation, in collaboration with Satellite Applications Catapult.

Edoardo Nemni (United Nations Satellite Centre (UNOSAT))

Edoardo Nemni is a Machine Learning Researcher at the United Nations Institute of Training and Research Operational Satellite Application Programme (UNITAR-UNOSAT). His research focus lies on apply deep learning algorithms to satellite imagery for disaster response such as satellite-derived flood analysis, shelter mapping, building footprints, damage assessment, and more. His current project is FloodAI: an end-to-end fully automated pipeline whereby satellite images of flood-prone areas are automatically downloaded and processed to output disaster maps.

Raul Ramos-Pollán (Universidad de Antioquia)

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