用廉价卫星图估算停车库占用率，实现公平城市出行分析
A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation
- 基于周六满、周日空的规律，用粗略时间标签训练对比模型
- 在大型停车场上达到0.92的AUC，无需昂贵高清图像
- 适合关注弱势群体出行与资源分配的研究者使用
高分辨率遥感影像标注数据稀缺且成本高昂，尤其在低收入地区更为突出。本研究提出一种弱监督框架，利用3米分辨率卫星图像估算停车场占用情况。通过假设德国主要超市和五金店的停车场在周六通常饱和、周日空置，采用粗粒度时间标签训练成对比较模型，在大型停车场上实现了0.92的AUC。该方法显著减少对高成本高清图像的依赖，具备可扩展性，适用于城市出行模式分析。此外，该方法可推广至评估弱势社区的交通流动与资源分配，为改善其福祉提供数据支持。
原文摘要 · Abstract (English)
The scarcity and high cost of labeled high-resolution imagery have long challenged remote sensing applications, particularly in low-income regions where high-resolution data are scarce. In this study, we propose a weak supervision framework that estimates parking lot occupancy using 3m resolution satellite imagery. By leveraging coarse temporal labels -- based on the assumption that parking lots of major supermarkets and hardware stores in Germany are typically full on Saturdays and empty on Sundays -- we train a pairwise comparison model that achieves an AUC of 0.92 on large parking lots. The proposed approach minimizes the reliance on expensive high-resolution images and holds promise for scalable urban mobility analysis. Moreover, the method can be adapted to assess transit patterns and resource allocation in vulnerable communities, providing a data-driven basis to improve the well-being of those most in need.
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