Multi-Temporal Landslide Mapping Nepal

Convolutional neural networks and deep learning models have recently been investigated, making it possible to quickly and accurately map landslides, but they haven't been used for multi-temporal landslide mapping in the Himalayas yet. A small landslide inventory across a small region was used for training the earlier models' supervised learning methodology, which was then applied to predict landslides in the area. We suggest a new technique that uses geographically distinct training samples to develop a common methodology that can be applied to develop multi-temporal landslide inventories. In the study region of the Rasuwa district of Nepal, MT landslide inventories are created using RapidEye pictures with a spatial resolution of 5 meters.