Automatic detection of earthquake triggered landslides using Sentinel-1 SAR imagery based on deep learning

Abstract

Earthquake Triggered Landslides (ETLs) are serious secondary hazards of earthquakes, causing severe casualties and property losses, and their rapid, automated and accurate detection is of great value. Due to the all-day and all-weather imaging capability of Synthetic Aperture Radar (SAR), ETL detection using SAR images is promising but faces the problem of insufficient accuracy. In this study, we proposed a deep learning-based ETL detection method to address this problem. Firstly, published ETL inventories and SAR images are combined to generate high-quality training datasets. Then, a landslide detection network of Multi-level Features Effective Weighting and Fusion (MFEWF) is proposed to effectively extract and fuze multi-level landslide features to identify SAR pixels within ETLs. Finally, the ETL boundary is determined based on these identified SAR pixels. This method is verified through three earthquake cases: the 2017 Mainling, China earthquake, the 2018 Palu, Indonesia earthquake and the 2018 Papua New Guinea earthquake. Results show that our method can effectively identify landslide boundaries with high accuracy (88.8%, 81.4% and 82.4% for the three cases), obviously outperforming other deep learning frameworks (e.g. DeepLabV3+). Using Sentinel-1 imagery to achieve such high accuracy in landslide detection, this study will improve emergency response to landslide disasters following earthquakes.