While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce.

翻译：尽管衡量社会经济指标对于地方政府制定明智政策至关重要，但此类指标通常在市级等细粒度层面难以获取。本研究利用基于深度学习的卫星图像预测来弥补这一差距。我们提出一种方法，通过捕捉基于地面实况在较大区域内观察到的分布行为，为每张卫星图像分配社会经济评分。我们训练了一个序数回归评分模型，并调整评分以遵循区域内及区域间的常见幂律分布。基于韩国官方统计数据的评估表明，我们的方法在预测市级和网格层面的人口与就业规模方面优于先前模型。该方法在不均衡发展的地区也表现出稳健性能，这表明其在缺乏可靠细粒度数据的发展中国家具有应用潜力。