A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913909083996160 |
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| author | Aidoo, Theophilus Koebe, Till Maurya, Akansh Shrestha, Hewan Weber, Ingmar |
| author_facet | Aidoo, Theophilus Koebe, Till Maurya, Akansh Shrestha, Hewan Weber, Ingmar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation Aidoo, Theophilus Koebe, Till Maurya, Akansh Shrestha, Hewan Weber, Ingmar Computer Vision and Pattern Recognition Computers and Society 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. |
| title | A Weak Supervision Learning Approach Towards an Equitable Mobility Estimation |
| topic | Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2505.04229 |