Back to Search
Start Over
Comparison between U-shaped structural deep learning models to detect landslide traces.
- Source :
-
The Science of the total environment [Sci Total Environ] 2024 Feb 20; Vol. 912, pp. 169113. Date of Electronic Publication: 2023 Dec 07. - Publication Year :
- 2024
-
Abstract
- Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023. Published by Elsevier B.V.)
Details
- Language :
- English
- ISSN :
- 1879-1026
- Volume :
- 912
- Database :
- MEDLINE
- Journal :
- The Science of the total environment
- Publication Type :
- Academic Journal
- Accession number :
- 38065499
- Full Text :
- https://doi.org/10.1016/j.scitotenv.2023.169113