Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps
Kyeongjin Ahn, YongHun Suh, Sungwon Han, Jeasurk Yang, Hannes Taubenb\"ock, Meeyoung Cha

TL;DR
This study presents a semi-supervised AI framework using remote sensing to map shelters in refugee camps, revealing critical insights into shelter expansion, population density, and WASH accessibility disparities over time.
Contribution
It introduces a novel semi-supervised segmentation method with SAM for shelter detection in complex environments, enhancing accuracy and enabling detailed WASH accessibility analysis.
Findings
Shelter detection improved by 4.9% F1-score over baselines.
Shelter expansion stabilized after 2020, but population growth reduced per capita space.
WASH accessibility declined from 2022 to 2025, with gender disparities evident.
Abstract
Lack of access to Water, Sanitation, and Hygiene (WASH) services is a major public health concern in refugee camps, where extreme crowding accelerates the spread of communicable diseases. The Rohingya settlements in Cox's Bazar, Bangladesh, exemplify these conditions, with large populations living under severe spatial constraints. We develop a semi-supervised segmentation framework using the Segment Anything Model (SAM) to map shelters from multi-temporal sub-meter remote sensing imagery (2017-2025), improving detection in complex camp environments by 4.9% in F1-score over strong baselines. The detected shelter maps show that shelter expansion stabilized after 2020, whereas continued population growth reduced per capita living space by approximately 14% between 2020 and 2025. WASH accessibility, measured with an enhanced network-based two-step floating catchment area (2SFCA) method,…
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Taxonomy
TopicsClimate Change, Adaptation, Migration · Asian Geopolitics and Ethnography · Child Nutrition and Water Access
