The DOI matches its Crossref record.
Map inference in the face of noise and disparity
Abstract
This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations. Additionally, while modern GPS receivers typically produce high-quality location estimates, errors over 100 meters are not uncommon, especially near tall buildings or under dense tree coverage. Combined, GPS trace disparity and error present a formidable challenge for the current state of the art in map inference. By tuning the parameters of existing algorithms, a user may choose to remove spurious roads created by GPS noise, or admit less-frequently traveled roads, but not both.
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Citations
Cited by
RoadTracer: Automatic Extraction of Road Networks from Aerial Images
Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., & DeWitt, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00496 · Cited by 359
Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., & Madden, S. (2018) · Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/3274895.3274927 · Cited by 21
Metadata
- Authors
- Biagioni, J., & Eriksson, J.
- Venue
- Proceedings of the 20th International Conference on Advances in Geographic Information Systems
- Publisher
- ACM
- Published
- 2012-11-06
- Pages
- 79-88
- OpenAlex
- W2071091794
- Cited by
- 205
- References
- 0
- FWCI
- 24.95
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Map inference in the face of noise and disparity. https://app.cochat.ai/papers/10.1145/2424321.2424333
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- Biagioni, J., & Eriksson, J. (2012). Map inference in the face of noise and disparity. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. https://doi.org/10.1145/2424321.2424333
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
