Accelerating Cross-Scene Co-Seismic Landslide Detection Through Progressive Transfer Learning and Lightweight Deep Learning Strategies

Sudden co-seismic landslides strike, causing widespread devastation and demanding a rapid response. The swift and accurate acquisition of landslide information is essential for effective disaster relief. Deep learning (DL)-based computer-aided interpretation methods have emerged as cutting-edge tools for landslide detection. Nevertheless, traditional DL approaches face limitations, such as high annotation costs, slow processing speeds, and low generalizability, rendering them unsuitable for rapid co-seismic landslide recognition tasks. This study presents a progressive approach for co-seismic landslide detection. First, we develop a Multi-scale Feature Fusion Lightweight Neural Network (MFFLnet), achieving exceptional generalizability and speed while maintaining precision. Second, we employ the deep transfer learning (TL) strategy, enabling MFFLnet to leverage prior landslide knowledge from a source domain and a refined data augmentation algorithm to combat overfitting. The proposed methodology is implemented in two co-seismic landslide scenes in Hokkaido, Japan, and Luding, China. Experimental results demonstrate that the proposed method exhibits outstanding performance in regional landslide recognition and robust performance across different co-seismic landslide detection scenarios. Our approach proves competitive in efficient co-seismic landslide disaster recognition and cross-scene identification, showcasing significant applicability in the face of rapid response demands.