GDP Estimation using a Deep Learning Fusion Model for Multi-Source Remote Sensing Data

In many developing countries, obtaining accurate and high-resolution GDP estimates is crucial for guiding economic policy and challenging due to limited data availability. This study introduces a novel deep learning fusion model to estimate the aggregated values of the Gross Domestic Product (GDP) in Brazil on a high spatial resolution grid of 1 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. This is done by a combination of remote sensing data, specifically optical imagery and nighttime light emissions. The model processes these data streams separately before fusing them for GDP prediction. This approach allows for the extraction of both physical and socioeconomic features relevant to economic activity, providing valuable insights for economic planning and policy making. Our fusion model achieved high r<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of up to 0.75 and was trained and tested in 29 Brazilian cities, demonstrating its effectiveness and scalability for large-scale urban economic estimations.