Improvement of large-scale-region landslide susceptibility mapping accuracy by transfer learning

Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment. However, the considerable time and financial burdens of landslide inventories often result in persistent data scarcity, which frequently impedes the generation of accurate and informative landslide susceptibility maps. Addressing this challenge, this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically. Notably, the proposed model, calibrated with the warmup-cosine annealing (WCA) learning rate strategy, demonstrated remarkable predictive capabilities, particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region. This is evidenced by the area under the receiver operating characteristic curve (AUC) values, which exhibited significant improvements of 51.00%, 24.40% and 2.15%, respectively, compared to a deep learning model, in contexts where only 1%, 5% and 10% of data from the target region were used for retraining. Simultaneously, there were reductions in loss of 16.12%, 27.61% and 15.44%, respectively, in these instances.