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Automatic recognition and segmentation methods now become the essentialrequirement in identifying co-seismic landslides, which are fundamental fordisaster assessment and mitigation in large-scale earthquakes. This approachused to be carried out through pixel-based or object-oriented methods. However,due to the massive amount of remote sensing data, variations in differentearthquake scenarios, and the efficiency requirement for post-earthquakerescue, these methods are difficult to develop into an accurate, rapid,comprehensive, and general (cross-scene) solution for co-seismic landsliderecognition. This paper develops a robust model, Dense Feature Pyramid withEncoder-decoder Network (DFPENet), to understand and fuse the multi-scalefeatures of objects in remote sensing images. The proposed method achieves acompetitive segmentation accuracy on the public ISPRS 2D Semantic. Furthermore,a comprehensive and widely-used scheme is proposed for co-seismic landsliderecognition, which integrates image features extracted from the DFPENet model,geologic features, temporal resolution, landslide spatial analysis, andtransfer learning, while only RGB images are used. To corroborate itsfeasibility and applicability, the proposed scheme is applied to twoearthquake-triggered landslides in Jiuzhaigou (China) and Hokkaido (Japan),using available pre- and post-earthquake remote sensing images.

DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides