Semi-Supervised Segmentation for Mapping Urban Expansion and Hazard Exposure in Lima, Peru

Abstract. Urban expansion in rapidly growing cities increases exposure to natural hazards but remains difficult to monitor in regions with limited data. This challenge is amplified in places such as Metropolitan Lima, where global datasets of urban areas lack precision along complex and rapidly changing city boundaries. As a result, recent growth in informal and peripheral zones is not well defined. This study introduces a practical application of a semi-supervised mapping approach that combines satellite imagery with partially labeled information and targeted manual refinement to identify new built-up areas in Metropolitan Lima from 2016 to 2025. The method improves the detection of small and fragmented structures, including emerging informal settlements that global datasets frequently miss. Results show that Metropolitan Lima expanded by approximately 76 km² during the study period. A portion of this growth occurred in coastal zones exposed to tsunamis, in areas with medium to high landslide susceptibility, and on soil types where strong ground shaking is amplified during large earthquakes. These findings highlight the continued concentration of people and infrastructure in hazard-prone terrain.