Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion

Kurzfassung

Landslide mapping is critically important for providing detailed spatial information on hazard extent in a timely manner that ultimately contributes to the protection of human lives and critical infrastructure. In the context of increasing demands for scalable and automated solutions, Earth Observation (EO) data coupled with deep learning offer great potential to enhance the speed and accuracy of emergency mapping. This study explores the utility of a deep learning model with the U-Net architecture for automated landslide mapping using data from optical Sentinel-2 and Synthetic Aperture Radar (SAR) Sentinel-1 satellites. We investigate the effectiveness of various optical (visible, near-infrared, and short-wave infrared) and SAR-derived features (backscatter coefficients, polarimetric features, interferometric coherence), used both independently and in combination. Additionally, we assess the impact of increasing the number of pre-/post-event SAR observations on classification performance. The U-Net models are trained and tested using globally distributed and limited reference data (563 unique patches). Optical features consisted of one pre-/post-event feature, whereas SAR features had three for each reference sample. Our analysis shows that the highest classification accuracies are consistently achieved using optical features (F1-score of 0.83 with visible, near-, and short-wave infrared bands). No substantial improvements were recorded when SAR features were combined with optical features. The usage of the most common optical features (visible and near-infrared) shows the lowest accuracies compared to their combination of short-wave infrared or red-edge bands. Increasing the number of pre-/post-event SAR features improves the SAR-based accuracies. To promote further advancements in automated landslide mapping using deep learning, the landslide reference dataset generated in this study is freely available at (https://doi.org/10.5281/zenodo.15284357).