A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation
This provides a scalable method for urban mobility analysis in vulnerable communities, though it is incremental as it adapts existing weak supervision techniques to a specific domain.
The authors tackled the problem of estimating parking lot occupancy in low-income regions where high-resolution satellite imagery is scarce and expensive by developing a weak supervision framework using 3m resolution imagery and coarse temporal labels. Their pairwise comparison model achieved an AUC of 0.92 on large parking lots, reducing reliance on costly high-resolution data.
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.