Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data.

ABSTRACT: Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3-5 m to systematically genera

SUBMITTER: Bhuyan K 

PROVIDER: S-EPMC9813262 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature