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

TL;DR

This paper demonstrates that deep learning models can effectively detect landslides using SAR datacubes, enabling rapid, early identification crucial for emergency response, with improved accuracy when combining SAR data with terrain information.

Contribution

The study provides a publicly available SAR datacube dataset for landslides and shows the feasibility of deep learning for rapid landslide detection using SAR data, incorporating terrain information for better accuracy.

Findings

Deep learning models achieved over 0.7 AUPRC in landslide detection.

Additional satellite passes improve detection performance.

Combining SAR data with terrain information enables early landslide detection.

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.