Comparative Evaluation of State-of-the-Art Semantic Segmentation Networks for Long-Term Landslide Map Production.

ABSTRACT: The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-

SUBMITTER: Hu Z 

PROVIDER: S-EPMC10674776 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature