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 concepts encoding typical urban and natural area patterns 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：利用深度学习从卫星图像预测社会经济指标已成为日益流行的研究方向。基于概念的后验解释可推动这些模型在政策制定中的更广泛应用，因其能够通过人类直观理解的视觉概念来解读社会经济结果。本文研究了使用额外任务特定对比损失的表征学习与后解释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区域和任务上的结果表明，任务特定的预训练会根据社会经济结果对潜在空间嵌入施加连续排序。这提升了模型的可解释性，使模型的潜在空间能够将编码典型城市和自然区域模式的概念与社会经济结果的连续区间相关联。此外，我们通过分析模型对社会经济结果区间的概念敏感性，展示了该方法如何为城市研究提供新的见解。