Village-level poverty identification using machine learning, high-resolution images, and geospatial data

Tracking progress in poverty alleviation and promptly identifying the distribution of poor areas are critical for strategic policy interventions, especially for regions with poor statistical systems. The massive satellite imagery and geospatial data provide great opportunities for timely and cost-effective socioeconomic evaluations. However, existing research on poverty identification is mostly based on satellite images, and the potential of combined multi-source geospatial data on poverty identification has not been fully explored. Here, we propose an approach that evaluates how village-level poverty can be identified by integrating high-resolution imagery (HRI), point-of-interest (POI), OpenStreetMap (OSM), and digital surface model (DSM) data. The study area included 338 villages from Yunyang County, located in Hubei Province, central China. We extracted the explanatory variables indicating access to facilities and services, agricultural production conditions, village construction, and the spatial distribution of village settlements from the HRI, POI, OSM, and DSM data. The random forest algorithm was then used to model the relationship between village-level poverty and explanatory variables. The results demonstrated a 54% accuracy in the prediction of village-level poverty; the best prediction performance (72%) was observed for the villages categorized as poor. The built-up land proportion and the time cost to the facilities and services contributed the most to the identification of village-level poverty, while the proxy variables of agricultural production conditions contributed the least. This study provides an approach to village-level poverty identification using satellite imagery and geospatial data and proves that the data employed in this study could identify the poorest areas that are highly coupled with natural geographical conditions and backward public services