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。

对社会经济结果的视觉概念解释的对比预训练

Contrastive Pretraining for Visual Concept Explanations of Socioeconomic  Outcomes

Understanding the link between urban planning and commuting flows is crucial
for guiding urban development and policymaking. This research, bridging
computer science and urban studies, addresses the challenge of integrating
these fields with their distinct focuses. Traditional urban studies methods,
like the gravity and radiation models, often underperform in complex scenarios
due to their limited handling of multiple variables and reliance on overly
simplistic and unrealistic assumptions, such as spatial isotropy. While deep
learning models offer improved accuracy, their black-box nature poses a
trade-off between performance and explainability -- both vital for analyzing
complex societal phenomena like commuting flows. To address this, we introduce
TransFlower, an explainable, transformer-based model employing flow-to-flow
attention to predict urban commuting patterns. It features a geospatial encoder
with an anisotropy-aware relative location encoder for nuanced flow
representation. Following this, the transformer-based flow predictor enhances
this by leveraging attention mechanisms to efficiently capture flow
interactions. Our model outperforms existing methods by up to 30.8% Common Part
of Commuters, offering insights into mobility dynamics crucial for urban
planning and policy decisions.

通过引入 TransFlower，一种可解释的、基于 Transformer 的模型，使用流量 - 流量注意力来预测城市通勤模式，我们的研究在运动力学方面取得了 30.8% 的改进，为城市规划和政策决策提供了重要的移动动态洞察。

逐流注意力的可解释 Transformer 模型用于通勤流预测

TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow  Attention for Commuting Flow Prediction

The visual dimension of cities has been a fundamental subject in urban
studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim,
and Jacobs. Several decades later, big data and artificial intelligence (AI)
are revolutionizing how people move, sense, and interact with cities. This
paper reviews the literature on the appearance and function of cities to
illustrate how visual information has been used to understand them. A
conceptual framework, Urban Visual Intelligence, is introduced to
systematically elaborate on how new image data sources and AI techniques are
reshaping the way researchers perceive and measure cities, enabling the study
of the physical environment and its interactions with socioeconomic
environments at various scales. The paper argues that these new approaches
enable researchers to revisit the classic urban theories and themes, and
potentially help cities create environments that are more in line with human
behaviors and aspirations in the digital age.

本文主要阐述了城市视觉信息在城市研究中的重要性以及新的图像数据和 AI 技术如何改变了城市研究的方式并引入了 Urban Visual Intelligence 的概念框架，使得城市的物理环境和社会经济环境在各种尺度上相互作用并研究可能有助于城市在数字时代创造更符合人类行为和愿望的环境。