Multimodal Poverty Prediction via Satellite Imagery and Socioeconomic Data

Multimodal Poverty Prediction via Satellite Imagery and Socioeconomic Data offers a comprehensive, mathematically sound treatment of combining Earth observation datasets with ground-truth demographic metrics. As remote sensing technologies and computational spatial analysis rapidly evolve, integrating unstructured visual imagery with structured socioeconomic indicators has become a critical technical frontier for high-resolution poverty mapping. This monograph details the computational workflows, feature fusion architectures, and spatial statistical frameworks required to merge heterogeneous inputs into accurate predictive models. Covering multi-spectral satellite imagery, nighttime lights data, land-use classification, national census metrics, and survey data, the text addresses spatial non-stationarity and data sparsity. Designed for data engineers, remote sensing specialists, and computational spatial analysts, this work presents robust methodologies for building quantitative tools.