Integrating Remote Sensing and Machine Learning for Monitoring Urban Growth in Seismically Active Regions: A Case Study of the Lembang Fault Zone, West Java, Indonesia

Urban growth monitoring is crucial for understanding the dynamics of land use changes, particularly in regions prone to geological hazards, such as the Lembang fault zone in West Java, Indonesia. This study uses remote sensing data and Geographic Information System (GIS) techniques to analyze the spatiotemporal changes in Land Use and Land Cover (LULC) from 2017 to 2023. By utilizing the ArcGIS Living Atlas of the World, high-resolution LULC data were extracted and analyzed to quantify the expansion of built-up areas, cropland, and tree cover over the specified period. Additionally, machine learning techniques were explored using Google Earth Engine, applying a Random Forest classifier to assess the potential for automated LULC classification in this region. The analysis reveals a consistent increase in urban areas, indicating significant development activities within the fault zone. The built area in the zone increased by 24.96% in six years. The findings underscore the effectiveness of integrating GIS and remote sensing approaches and machine learning for monitoring urban growth in seismically active areas, providing essential insights for urban planning and disaster mitigation efforts.