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Hashemi Fesharaki, Seyedeh Farnoosh

University of British Columbia

Digital maps play a crucial role in various applications such as navigation, fleet management, and ride-sharing, necessitating their accuracy and timely updates. While the majority of geospatial databases (GDBs) provide high-quality information, their data is (i) limited to specific regions and/or (ii) missing some entities even in their covered areas. Map conflation is the process of the augmentation of a GDB using another GDB to conflate missing spatial features. Existing map conflation methods suffer from two main limitations: (1) They are designed for the conflation of linear objects (e.g., road networks) and cannot simply be extended to non-linear objects, thus missing information about most entities in the map. (2) They are heuristic algorithmic approaches that are based on pre-defined rules, unable to learn entities matching in a data-driven manner. To address these limitations, we design MINOR (Map Conflation via Knowledge Graph Representation), a machine-learning approach consisting of three parts: (1) Knowledge Graph Construction: where each GDB is represented by a knowledge graph (2) Map Matching: where we use a knowledge graph alignment method as well as a geospatial feature encoder to match entities in obtained knowledge graphs. (3) Map Merging: to consistently merge matched entities in the previous modules, we use a mixed integer linear programming formulation that fully merges the GDBs without adding any inconsistencies. Our experimental evaluation shows that not only MINOR achieves outstanding performance compared to state-of-the-art and baselines in map conflation tasks, but each of its modules (e.g., Map Matching and Map Merging) separately outperforms traditional matching and merging methods.

Text

2023-08-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85388

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/