Résumé
Monitoring local socio-economic variations is essential for tracking progress toward sustainable development goals. However, measuring these variations can be challenging, as it requires data collection at least twice, which is both expensive and time-consuming. To address this issue, researchers have proposed remote sensing and deep learning methods to predict socio-economic indicators. However, subtracting two predicted socio-economic indicators from different dates leads to inaccurate results. We propose a novel method for predicting socio-economic variations using satellite image time series to achieve more reliable predictions. Our method leverages both spatial and temporal information to enhance the final prediction. In our experiments, we observed that it outperforms state-of-the-art methods.