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arXiv · 2505.04229

A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation

Theophilus Aidoo·Till Koebe·Akansh Maurya·Hewan Shrestha·Ingmar Weber
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Abstract

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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Theophilus Aidoo, Till Koebe, Akansh Maurya, Hewan Shrestha, Ingmar Weber. 2025-05-07. A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation. https://doi.org/10.36190/2025.04

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