Mapping Reduced Accessibility to WASH Facilities in Rohingya Refugee Camps With Sub-Meter Imagery

• URL: http://arxiv.org/abs/2511.07231v3
• Date: Tue, 18 Nov 2025 13:40:47 GMT
• Title: Mapping Reduced Accessibility to WASH Facilities in Rohingya Refugee Camps With Sub-Meter Imagery
• Authors: Kyeongjin Ahn, YongHun Suh, Sungwon Han, Jeasurk Yang, Hannes Taubenböck, Meeyoung Cha,
• Abstract summary: Access to Water, Sanitation, and Hygiene (WASH) services remains a major public health concern in refugee camps.<n>This study introduces a remote sensing-driven framework to quantify WASH accessibility-specifically to water pumps, latrines, and bathing cubicles.<n>Using sub-meter satellite images, we develop a semi-supervised segmentation framework that achieves an F1-score of 76.4% in detecting individual refugee shelters.
• Score: 13.938740503013257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Access to Water, Sanitation, and Hygiene (WASH) services remains a major public health concern in refugee camps. This study introduces a remote sensing-driven framework to quantify WASH accessibility-specifically to water pumps, latrines, and bathing cubicles-in the Rohingya camps of Cox's Bazar, one of the world's most densely populated displacement settings. Detecting refugee shelters in such emergent camps presents substantial challenges, primarily due to their dense spatial configuration and irregular geometric patterns. Using sub-meter satellite images, we develop a semi-supervised segmentation framework that achieves an F1-score of 76.4% in detecting individual refugee shelters. Applying the framework across multi-year data reveals declining WASH accessibility, driven by rapid refugee population growth and reduced facility availability, rising from 25 people per facility in 2022 to 29.4 in 2025. Gender-disaggregated analysis further shows that women and girls experience reduced accessibility, in scenarios with inadequate safety-related segregation in WASH facilities. These findings suggest the importance of demand-responsive allocation strategies that can identify areas with under-served populations-such as women and girls-and ensure that limited infrastructure serves the greatest number of people in settings with fixed or shrinking budgets. We also discuss the value of high-resolution remote sensing and machine learning to detect inequality and inform equitable resource planning in complex humanitarian environments.

Related papers

• Spatial Disparities in Fire Shelter Accessibility: Capacity Challenges in the Palisades and Eaton Fires [2.4146666103958077]
  The study investigates issues of accessibility to shelters during the Palisades and Eaton Fires which started in January 2025 in Southern California.<n>Results reveal severe shelter shortages and pronounced inequities in access to shelters, particularly in geographically isolated regions and mountainous areas.
  arXiv  Detail & Related papers  (2025-06-07T13:55:27Z)
• Enhanced Drought Analysis in Bangladesh: A Machine Learning Approach for Severity Classification Using Satellite Data [0.0]
  Drought poses a pervasive environmental challenge in Bangladesh, impacting agriculture, socio-economic stability, and food security.<n>Traditional drought indices, such as the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), often overlook crucial factors like soil moisture and temperature.<n>We propose a satellite data-driven machine learning framework to classify drought across 38 districts of Bangladesh.
  arXiv  Detail & Related papers  (2025-06-05T07:17:43Z)
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response [50.76124284445902]
  Building damage assessment (BDA) is an essential capability in the aftermath of a disaster to reduce human casualties.<n>Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events.<n>We present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
  arXiv  Detail & Related papers  (2025-01-10T14:57:18Z)
• Tracking Progress Towards Sustainable Development Goal 6 Using Satellite Imagery [0.0]
  This study integrates Afrobarometer survey data, satellite imagery from Landsat 8 and Sentinel-2 to develop a modeling framework for evaluating access to piped water and sewage system across diverse African regions.<n>The modeling framework achieved notable accuracy, with over 96% for piped water and 97% for sewage system access classification.<n>This approach provides policymakers and stakeholders with an effective, scalable, and cost-efficient tool to pinpoint underserved areas requiring targeted intervention.
  arXiv  Detail & Related papers  (2024-11-28T12:13:46Z)
• Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
  This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
  arXiv  Detail & Related papers  (2024-02-14T13:20:24Z)
• IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [90.97859312029615]
  We propose an approach to estimate object count statistics over large geographies through sampling. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S.
  arXiv  Detail & Related papers  (2021-12-16T18:59:29Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)
• Mapping Access to Water and Sanitation in Colombia using Publicly Accessible Satellite Imagery, Crowd-sourced Geospatial Information and RandomForests [0.0]
  We present a scalable and inexpensive end-to-end WASH estimation workflow. We generate a map of WASH estimates at a granularity of 250m x 250m across the entire country of Colombia. The model was able to explain up to 65% of the variation in predicting access to water supply, sewage, and toilets.
  arXiv  Detail & Related papers  (2021-11-07T17:36:50Z)
• BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning [77.08836528980248]
  Social distancing, an essential public health measure, has gained significant attention since the outbreak of the COVID-19 pandemic. In this work, the problem of visual social distancing compliance assessment in busy public areas with wide field-of-view cameras is considered. A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced. A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated.
  arXiv  Detail & Related papers  (2021-10-10T23:56:37Z)
• A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs [0.0]
  One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
  arXiv  Detail & Related papers  (2021-07-29T18:22:34Z)
• Estimating Displaced Populations from Overhead [12.392630992454396]
  We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery.
  arXiv  Detail & Related papers  (2020-06-25T16:45:11Z)
• Generating Interpretable Poverty Maps using Object Detection in Satellite Images [80.35540308137043]
  We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
  arXiv  Detail & Related papers  (2020-02-05T02:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.