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Road Networks Matching Supercharged With Embeddings

Published: 22 November 2024 Publication History

Abstract

This research introduces a novel Road Network Embeddings Matching (RNEM) method for road network matching in map conflation tasks, addressing key challenges in integrating diverse map datasets. Traditional methods like Delimited Stroke Oriented (DSO) and Hootenanny face difficulties with disparities in geometric, semantic, and topological information. RNEM leverages embeddings derived from these features, significantly improving accuracy, precision, recall, and F1 score. Using pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), RNEM captures semantic and topological information, while geometric embeddings are generated through resampling and normalization of polylines. Experiments on Munich datasets show that RNEM outperforms existing methods by 3.2% in accuracy. This method represents the irst approach to incorporate semantic and topological information using NLP techniques, offering a comprehensive solution for map conflation, benefiting initiatives such as the Overture Maps Foundation.

References

[1]
Esther M. Arkin, L. Paul Chew, Daniel P. Huttenlocher, Klara Kedem, and Joseph B. M. Mitchell. 1991. An efficiently computable metric for comparing polygonal shapes. IEEE Trans. Pattern Anal. Mach. Intell. 13 (1991), 209--216. https://api.semanticscholar.org/CorpusID:8247618
[2]
Roberto Canavosio-Zuzelski, Jason Surratt, Drew Bower, Joseph Governski, and Matthew Sorenson. 2015. Hootenanny: web enabeled geospatial vector-data conflation and map generation. In ASPRS 2015 annual conference. 4--8.
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics. https://api.semanticscholar.org/CorpusID:52967399
[4]
Zhongliang Fu, Liang Fan, Zhiqiang Yu, and Kaichun Zhou. 2018. A Moment-Based Shape Similarity Measurement for Areal Entities in Geographical Vector Data. ISPRS International Journal of Geo-Information 7, 6 (2018). https://doi.org/10.3390/ijgi7060208
[5]
Yingjie Hu e.t. al Gengchen Mai, Krzysztof Janowicz. 2022. A review of location encoding for GeoAI: methods and applications. International Journal of Geographical Information Science 36, 4 (2022), 639--673. https://doi.org/10.1080/13658816.2021.2004602 arXiv:https://doi.org/10.1080/13658816.2021.2004602
[6]
MICHAEL F. GOODCHILD and GARY J. HUNTER. 1997. A simple positional accuracy measure for linear features. International Journal of Geographical Information Science 11, 3 (1997), 299--306. https://doi.org/10.1080/136588197242419 arXiv:https://doi.org/10.1080/136588197242419
[7]
Vladimir I. Levenshtein. 1965. Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics. Doklady 10 (1965), 707--710. https://api.semanticscholar.org/CorpusID:60827152
[8]
Maureen P Lynch and Alan J Saalfeld. 1985. Conflation: Automated map compilation---a video game approach. In Proceedings Auto-Carto, Vol. 7. Symposium Directorate, Auto-Carto, 343--352. https://cartogis.org/docs/proceedings/archive/auto-carto-7/pdf/conlation-automated-map-compilation-a-video-game-approach.pdf
[9]
Grant McKenzie, Krzystof Janowicz, and Benjamin Adams. 2014. A weighted multi-attribute method for matching user-generated Points of Interest. Cartography and Geographic Information Science 41 (01 2014), 125--137. https://doi.org/10.1080/15230406.2014.880327
[10]
Liqiu Meng Meng Zhang and Joachim Bobrich. 2010. A road-network matching approach guided by 'structure'. Annals of GIS 16, 3 (2010), 165--176. https://doi.org/10.1080/19475683.2010.513154 arXiv:https://doi.org/10.1080/19475683.2010.513154
[11]
Ana-Maria Olteanu Raimond and Sébastien Mustière. 2008. Data Matching - a Matter of Belief. In Headway in Spatial Data Handling, Anne Ruas and Christopher Gold (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 501--519.
[12]
Lawrence Philips. 2000. The Double Metaphone Search Algorithm. C/C++ Users Journal 18 (06 2000), 38--43.
[13]
ALAN SAALFELD. 1988. Conflation Automated map compilation. International journal of geographical information systems 2, 3 (1988), 217--228. https://doi.org/10.1080/02693798808927897 arXiv:https://doi.org/10.1080/02693798808927897
[14]
Abhilshit Soni and Sanjay Boddhu. 2022. Finding map feature correspondences in heterogeneous geospatial datasets. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs (Seattle, Washington) (GeoKG '22). Association for Computing Machinery, New York, NY, USA, 7--16. https://doi.org/10.1145/3557990.3567590
[15]
Jianhua Wu, Yu Zhao, Mengjuan Yu, Xiaoxiang Zou, Jiaqi Xiong, and Xiang Hu. 2023. A new Voronoi diagram-based approach for matching multi-scale road networks. Journal of Geographical Systems 25 (04 2023), 1--25. https://doi.org/10.1007/s10109-023-00409-w
[16]
Emerson M. A. Xavier, Francisco J. Ariza-López, and Manuel A. Ureña Cámara. 2016. A Survey of Measures and Methods for Matching Geospatial Vector Datasets. ACM Comput. Surv. 49, 2, Article 39 (aug 2016), 34 pages. https://doi.org/10.1145/2963147
[17]
Meng Zhang and Liqiu Meng. 2008. Delimited Stroke Oriented Algorithm-Working Principle and Implementation for the Matching of Road Networks. Geographic Information Sciences 14, 1 (2008), 44--53. https://doi.org/10.1080/10824000809480638 arXiv:https://doi.org/10.1080/10824000809480638
[18]
Xiang Zhang, Tinghua Ai, Jantien Stoter, and Xi Zhao. 2014. Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS Journal of Photogrammetry and Remote Sensing 92 (2014), 147--163. https://doi.org/10.1016/j.isprsjprs.2014.03.010

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cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
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Published: 22 November 2024

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Author Tags

  1. Conflation
  2. Location Embeddings
  3. Road Network Representation
  4. Road Networks Matching
  5. Word Embeddings

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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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