A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they
often occur across large areas, landslide detection requires rapid and reliable automatic detection
approaches. Currently, deep learning (DL) approaches, especially different convolutional neural
network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge
accuracies in automatic landslide detection. However, these successful applications of various DL
approaches have thus far been based on very high-resolution satellite images (e.g., GeoEye and
WorldView), making it easier to achieve such high detection performances. In this study, we use freely
available Sentinel-2 data and ALOS digital elevation model to investigate the application of two wellknown FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide
detection. To our knowledge, this is the first application of FCN for landslide detection only from freely
available data. We adapt the algorithms to the specific aim of landslide detection, then train and test
with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng
Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches
to train the algorithms. Our results also contain a comprehensive transferability assessment achieved
through different training and testing scenarios in the three case studies. The highest f1-score value of
73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China’s holdout
testing area using the sample patch size of 64 × 64 pixels.