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

2025-11-10

AI Summary

In Bangladesh’s Cox’s Bazar Rohingya refugee camps, WASH facility accessibility has deteriorated continuously (2022–2025), with average population served per facility rising from 25 to 29.4; women face heightened access barriers due to inadequate safety and spatial segregation. Method: We propose a remote sensing–driven semi-supervised semantic segmentation framework integrating sub-meter satellite imagery and multi-temporal analysis to accurately extract individual shelters in high-density, irregularly structured camps (F1-score: 76.4%). This is coupled with gender-disaggregated accessibility modeling to quantify spatial inequities in facility distribution. Contribution/Results: The framework enables dynamic, demand-responsive resource allocation, offering a scalable methodology and empirical evidence for equitable WASH governance in fragile humanitarian settings.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Bias, Fairness & Privacy
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Mapping WASH facility accessibility in Rohingya refugee camps using satellite imagery
• Detecting individual refugee shelters in dense, irregular camp configurations
• Analyzing gender-disparities in WASH access with inadequate safety segregation

Innovation

• Semi-supervised segmentation framework for shelter detection
• Sub-meter satellite imagery for WASH accessibility mapping
• Gender-disaggregated analysis of facility accessibility