Integration of Machine Learning, Remote Sensing, and WebGIS for Landslide Hazard Potential Monitoring Yanuarsyah I, Hidayat J, Setiawan I, Agus S.B
Ibn Khaldun University of Bogor, Pakuan University, LSP MAPIN, IPB University
Abstract
This study integrates four case studies based on remote sensing and GIS in Indonesia, covering landslide susceptibility modeling using Random Forest, biomass estimation, spatial analysis of landlside hazard, and development of an interactive WebGIS. Each study employed different data sources, such as Sentinel imagery, Landsat 8 OLI, elevation model, and field survey data, with analytical methods including machine learning classification, vegetation index regression, spatial analysis scoring, and web mapping applications. The integration aims to build a unified framework for landslide hazard potential condition monitoring accessible to stakeholders in real time. Results indicate that the combination of machine learning and GIS improves disaster prediction accuracy and environmental information quality. Landslide susceptibility modeling achieved an AUC, with slope gradient and rainfall as the most influential variables. Biomass estimation with NDVI as a key predictor. Landslide hazard analysis identified high risk zones near rivers and lower lying areas, while the WebGIS successfully delivered interactive thematic maps for easier information access. The proposed integration framework supports the National Geospatial Data Infrastructure and promotes the use of AI, UAV, and remote sensing data for based policy making.
Keywords: Machine Learning, Random Forest, WebGIS, Remote Sensing, Multi-Hazard, NGDI
Topic: Topic C: Emerging Technologies in Remote Sensing
