Leveraging deep learning models to increase the representation of nomadic pastoralists in health campaigns and demographic surveillance.

ABSTRACT: Nomadic pastoralists are systematically underrepresented in the planning of health services and frequently missed by health campaigns due to their mobility. Previous studies have developed novel geospatial methods to address these challenges but rely on manual techniques that are too time and resource-intensive to scale on a national or regional level. To address this gap, we developed a computer vision-based approach to automatically locate active nomadic pastoralist settlements from satellite imagery. We curated labeled datasets of satellite images capturing approximately 1,000 historically active settlements in the Omo Valley of Ethiopia and the Samburu County of Kenya to train and evaluate deep learning models, studying their robustness to low spatial resolutions and limits in labeled

SUBMITTER: Liu B 

PROVIDER: S-EPMC12021161 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature