Deep learning models integrating multi-sensor and -temporal remote sensing to monitor landslide traces in Vietnam

Kinh Bac, Dang; Tuan Linh, Giang; Dang, Van Bao; Phan, Trinh; Truong, Hai; Ngo, Van Liem; Do, Trung Hieu; Dang, Nguyen Vu; Forino, Giuseppe

Abstract

Landslides pose significant threats to lives and public infrastructure in mountainous regions. Real-time landslide monitoring presents challenges for scientists, often involving substantial costs and risks due to challenging terrain and instability. Recent technological advancements offer the potential to identify landslide-prone areas and provide timely warnings to local populations when adverse weather conditions arise. This study aims to achieve three key objectives: (1) propose indicators for detecting landslides in both field and remote sensing images; (2) develop deep learning (DL) models capable of automatically identifying landslides from fusion data of Sentinel-1 (SAR) and Sentinel-2 (optical) images; and (3) employ DL-trained models to detect this natural hazard in specific regions of Vietnam. Twenty DL models were trained, utilizing three U-shaped architectures, which include U-Net and U-Net3+, combined with different data-fusion choices. The training data consisted of multi-temporal Sentinel images and increased the accuracy of DL models using Adam optimizer to 99% in landslide detection with low loss function values. Using two bands of the Sentinel-1 could not define the characteristics of landslide traces. However, the integration between Sentinel-2 data and these bands makes the landslide detection process more effective. Therefore, the authors proposed a consolidated strategy based on three models: (1) UNet using four S2-bands, (2) UNet3+ using four S2-bands, (3) UNet using four S2-bands and VV S1-band, and (4) UNet using four S2-bands and VH S1-band for fully detect landslides. This integrated strategy uses the capabilities of each model and overcomes model result constraints to better describe landslide traces in varied geographical locations.

Citation

Kinh Bac, D., Tuan Linh, G., Dang, V. B., Phan, T., Truong, H., Ngo, V. L., …Forino, G. (2024). Deep learning models integrating multi-sensor and -temporal remote sensing to monitor landslide traces in Vietnam. International Journal of Disaster Risk Reduction, 105, 104391. https://doi.org/10.1016/j.ijdrr.2024.104391

• Journal Article Type: Article
• Acceptance Date: Mar 7, 2024
• Online Publication Date: Mar 18, 2024
• Publication Date: 2024-04
• Deposit Date: Mar 25, 2024
• Publicly Available Date: Mar 19, 2026
• Journal: International Journal of Disaster Risk Reduction
• Print ISSN: 2212-4209
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 105
• Pages: 104391
• DOI: https://doi.org/10.1016/j.ijdrr.2024.104391
• Keywords: Geology; Safety Research; Geotechnical Engineering and Engineering Geology; Building and Construction