Enhancing digital twins with privacy-aware EO-ML methods

Digital Twins, dynamic virtual models, require granular spatiotemporal data often absent or anonymized in low- and middle-income regions. We propose a privacy-aware Earth Observation–Machine Learning framework that treats privacy-protected survey locations as a missing data problem, integrating multiple imputation with multi-temporal satellite imagery and recurrent convolutional neural networks. Applied to continent-wide poverty mapping in Africa, the method quantifies uncertainty, significantly improves predictive accuracy, and reduces biases introduced by location perturbation. The resulting high-resolution economic indicators support more reliable socioeconomic and environmental Digital Twins for policy analysis. This approach reconciles data privacy and utility, benefiting urban planning, economic forecasting, and sustainability initiatives.