Enhancing the Reliability, Performance, and Practical Value of Deep Learning Landslide Mapping via Pixel-Level Uncertainty Analysis
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Pixel-level uncertainty assessment is commonly overlooked in current deep learning-based landslide detection, which greatly limits the reliability, performance, and practical value of the results. To fill this gap, this study employs Monte Carlo Dropout to systematically quantify pixel-level uncertainty. Subsequently, it interprets how external factors, such as terrain, vegetation, clouds, and shadows, interfere with model predictions. Taking SegFormer as an example, this study further investigates how weight allocation across different scales influences the uncertainty. Finally, we introduce an uncertainty ranking-based FP rejection strategy coupled with an FN priority capture strategy to improve the efficiency of mapping results inspection, thus improving landslide identification performance. The results indicate that by removing only the top 10% of pixels with the highest uncertainty, the mIoU increases by at least 7%. This study greatly enhanced the reliability, performance, and practical value of remote sensing-based landslide mapping from a new perspective.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Enhancing the Reliability, Performance, and Practical Value of Deep Learning Landslide Mapping via Pixel-Level Uncertainty Analysis
- Date Crossref
- 20/08/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
China University of Geosciences Badong National Observation and Research Station of Geohazards pays non établi dans la noticeUniversité ou école supérieure
-
School of Earth and Planetary Sciences pays non établi dans la noticeUniversité ou école supérieure
-
Jiangxi Provincial Technology Innovation Center for Territorial Spatial Information Acquisition and Processing pays non établi dans la noticeInstitution
Badong National Observation and Research Station of Geohazards — China University of Geosciences, School of Earth and Planetary Sciences et Jiangxi Provincial Technology Innovation Center for Territorial Spatial Information Acquisition and Processing.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.