A Weak Supervision Learning Approach Towards an Equitable Parking Lot Occupancy Estimation

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.

@article{aidoo2025_2505.04229,
  title={ A Weak Supervision Learning Approach Towards an Equitable Parking Lot Occupancy Estimation },
  author={ Theophilus Aidoo and Till Koebe and Akansh Maurya and Hewan Shrestha and Ingmar Weber },
  journal={arXiv preprint arXiv:2505.04229},
  year={ 2025 }
}