Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911526257950720 |
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| author | Ahn, Kyeongjin Suh, YongHun Han, Sungwon Yang, Jeasurk Taubenböck, Hannes Cha, Meeyoung |
| author_facet | Ahn, Kyeongjin Suh, YongHun Han, Sungwon Yang, Jeasurk Taubenböck, Hannes Cha, Meeyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps Ahn, Kyeongjin Suh, YongHun Han, Sungwon Yang, Jeasurk Taubenböck, Hannes Cha, Meeyoung Computer Vision and Pattern Recognition 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. |
| title | Semi-supervised Shelter Mapping for WASH Accessibility Assessment in Rohingya Refugee Camps |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.07231 |