Machine-assisted map editing
Open accessAbstract
Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.
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Accurate and well-annotated maps can inform evacuation planning, retrofitting campaigns, and delivery of relief.
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From Tackling climate change with machine learning (2023) · cites this paper as [59]
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Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., & Madden, S.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Hamad bin Khalifa University (QA)
- MIT CSAIL
- Venue
- Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Publisher
- ACM
- Published
- 2018-11-06
- Pages
- 23-32
- OpenAlex
- W2900464026
- Cited by
- 21
- References
- 21
- Citations by year
- 2025: 1 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 2 · 2019: 3
- FWCI
- 5.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Keywords
- road network mapping, OpenStreetMap, aerial imagery, GPS tracks, user study
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