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.

利用深度学习从卫星图像中预测社会经济指标已成为一个越来越受欢迎的研究方向，我们研究了额外特定任务对比损失的表示学习与后期概念可解释性在社会经济研究中的相互关系，并通过两个不同地理位置和任务的结果表明，任务特定预训练对潜在空间嵌入按照社会经济结果的连续排序有所影响，从而提高了模型的可解释性。进一步地，我们阐述了分析模型对社会经济结果区间的概念敏感性如何为城市研究提供新的见解。