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.