Predicting socioeconomic indicators from satellite imagery with deep learning has become an increasingly popular research direction. Post-hoc concept-based explanations can be an important step towards broader adoption of these models in policy-making as they enable the interpretation of socioeconomic outcomes based on visual concepts that are intuitive to humans. In this paper, we study the interplay between representation learning using an additional task-specific contrastive loss and post-hoc concept explainability for socioeconomic studies. Our results on two different geographical locations and tasks indicate that the task-specific pretraining imposes a continuous ordering of the latent space embeddings according to the socioeconomic outcomes. This improves the model's interpretability as it enables the latent space of the model to associate urban concepts with continuous intervals of socioeconomic outcomes. Further, we illustrate how analyzing the model's conceptual sensitivity for the intervals of socioeconomic outcomes can shed light on new insights for urban studies.

翻译：通过深度学习从卫星图像预测社会经济指标已成为一个日益流行的研究方向。基于后 hoc 概念的解释可以成为这些模型在政策制定中更广泛采纳的重要一步，因为它们使得能够基于人类直观理解的视觉概念来解释社会经济结果。在本文中，我们研究了使用附加任务特定对比损失的表示学习与后 hoc 概念可解释性在社会经济研究中的相互作用。我们在两个不同地理位置和任务上的结果表明，任务特定的预训练根据社会经济结果对潜在空间嵌入施加了连续排序。这提升了模型的可解释性，因为它使模型的潜在空间能够将城市概念与社会经济结果的连续区间关联起来。此外，我们说明了分析模型对社会经济结果区间的概念敏感性如何能为城市研究揭示新的见解。