Mapping Refugee Camps with AI: A Benchmark Dataset and Baseline Models for Humanitarian Applications - Gupta, Amrita; Ortiz, Anthony; Fobi Nsutezo, Simone; Kebut, Duncan; Iyer, Seema; Dodhia, Rahul; Lavista Ferres, Juan M. (Proceedings of the Winter Conference on Applications of Computer Vision, 2025)
Over 6.6 million people worldwide live in refugee camps, most of which lack comprehensive, up-to-date maps. This hinders effective resource distribution, infrastructure planning, and disaster response in these environments. Automated mapping with aerial imagery offers a promising solution, capturing the detail needed for effective camp management, but it requires datasets that reflect the distinct characteristics of refugee camps. Existing building footprint datasets focus on urban or semi-urban areas leaving refugee camps–characterized by irregular layouts, diverse building sizes, and varied materials–underrepresented and poorly served by current models. This study introduces the KAKUMAAERIAL dataset, an open-source resource for humanitarian mapping. It pairs high-resolution aerial imagery from the Kakuma-Kalobeyei refugee camps in Kenya with annotations for buildings, solar panels, roof materials, and sanitation facilities. The dataset serves as a resource for benchmarking models on tasks crucial to humanitarian aid. Baseline machine learning models achieved strong performance on key tasks: building and solar panel segmentation (IoU of 0.848 and 0.813, respectively), roof material classification (accuracy of 85.6%), and toilet identification (accuracy of 97.8%). By applying these models to broader areas within the camps, the study provides actionable insights into camp infrastructure, including energy access and sanitation availability. This research demonstrates how geospatial technologies and machine learning can enable humanitarian organizations to improve operational efficiency while improving the living conditions and dignity of displaced populations.