Integrating remote sensing and artificial intelligence for landslide detection and susceptibility analysis along tourism routes in Da Bac district, Hoa Binh province, Vietnam - Dang Kinh, Bac; Hoang Huong, Thi Thu; Nguyen, Hieu; Vu, Kim Chi; Linh Giang, Tuan; Damien, Closson; Linh Nguyen, Thi Dieu; Ngan Do, Thi (Vietnam Journal of Earth Sciences, 2025)
The occurrence of natural disasters, especially with landslides, threatens mountainous districts and has serious consequences on local tourism development. Future disaster management must develop efficient innovative tools to control the rising frequency and intensity of landslides due to the impacts of economic development and climate change. Minimizing the risk and effects of these occurrences relies on the establishment of an optimal early warning system. This study focuses on the integration of artificial intelligence approaches to identify landslides and evaluate their susceptibility, with an emphasis on early warning systems on tourist routes in Da Bac district. As the first tool in the system, advanced deep learning models using satellite data at high resolution assist in identifying landslides. As a result, a developed DeepLab-v3 model demonstrated high performance by reaching 0.213 dice coefficient and 96.8% accuracy for landslide detection without restrictions from specific input resolution sizes. As the second tool, various machine learning tools, such as Random Forest and Support Vector Machine, utilize the identified landslide locations from the first tool to assess and map their susceptibility based on environmental and human-made factors. Accordingly, the study proposed an early warning system for landslide disaster management using real-time ecological factors and historical data. The proposed integrated system helps tourists and local communities take preventive actions that reduce landslide impacts, thus achieving safety goals in tourism activities, particularly in the Da Bac district of Hoa Binh province, Vietnam. It enhances strategies to minimize risk, increases the ability to predict landslide-prone tourist areas, and aids in implementing sustainable tourism in the future.