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

This paper develops a robust model, Dense Feature Pyramid with Encoder-decoder Network (DFPENet), to understand and fuse the multi-scale features of objects in remote sensing images. The proposed method achieves a competitive segmentation accuracy on the public ISPRS 2D Semantic. Furthermore, a comprehensive and widely-used scheme is proposed for co-seismic landslide recognition, which integrates image features extracted from the DFPENet model, geologic features, temporal resolution, landslide spatial analysis, and transfer learning, while only RGB images are used. To corroborate its feasibility and applicability, the proposed scheme is applied to two earthquake-triggered landslides in Jiuzhaigou (China) and Hokkaido (Japan), using available pre- and post-earthquake remote sensing images. The experiments show that the proposed scheme presents a new state-of-the-art performance in regional landslide identification, and performs well in different seismic landslide recognition tasks, though landslide boundary error is not considered. The proposed scheme demonstrates a competitive performance for high-precision, high-efficiency and cross-scene recognition of earthquake disasters, which may serve as a starting point for the application of deep learning methods in co-seismic landslide recognition.