用AI分析卫星图像，揭示罗兴亚难民营的住房与卫生设施不平等现状。
Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps
- 基于SAM模型的半监督分割，从多时相高分辨率影像中识别庇护所。
- 2020年后住房扩张停滞，人均空间下降14%，卫生设施负荷超国际标准。
- 女性获取卫生设施机会比男性低约27%，凸显性别不公，适合政策制定者参考。
缺乏饮水、卫生与个人卫生（WASH）服务是难民营中的重大公共卫生问题，极端拥挤加速传染病传播。孟加拉国科克斯巴扎尔的罗兴亚难民营正是此类情况的典型代表，大量人口在严重空间约束下生活。我们利用分割一切模型（SAM）开发了一种半监督分割框架，基于2017至2025年的多时相亚米级遥感影像，对庇护所进行映射，在复杂营地环境中相比强基线提升了4.9%的F1分数。检测结果表明，2020年后庇护所扩张趋于稳定，而持续的人口增长导致2020至2025年间人均居住空间减少约14%。通过改进的网络型两步浮游捕获区（2SFCA）方法评估的WASH可及性显示，2022至2025年间呈下降趋势，设施负载增加并超过全球基准。引入安全惩罚的性别细分情景进一步揭示显著不平等：女性可及性较男性低约27%。这些结果共同表明，遥感驱动的AI诊断可生成以公平为导向的证据，用于优先投资WASH基础设施，降低长期流离失所环境下的健康风险。
原文摘要 · Abstract (English)
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, declined from 2022 to 2025, increasing facility loads and exceeding global benchmarks. Gender-disaggregated scenarios that incorporate safety penalty further reveal pronounced inequities, with female accessibility approximately 27% lower than male. Together, these results demonstrate that remote sensing-driven AI diagnostics can generate equity-focused evidence to prioritize WASH investments and mitigate health risks in protracted displacement settings.
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