Omni geometry representation learning versus large language models for Geospatial Entity Resolution

Wijegunarathna, Kalana, Stock, Kristin and Jones, Christopher B. ORCID: https://orcid.org/0000-0001-6847-7575 2026. Omni geometry representation learning versus large language models for Geospatial Entity Resolution. Transactions in GIS 30 (1) , e70199. 10.1111/tgis.70199

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Abstract

The development, integration, and maintenance of geospatial databases rely heavily on efficient and accurate matching procedures of Geospatial Entity Resolution (ER). While resolution of points‐of‐interest (POIs) has been widely addressed, resolution of entities with diverse geometries has been largely overlooked. This is partly due to the lack of a uniform technique for embedding heterogeneous geometries seamlessly into a neural network framework. Existing neural approaches simplify complex geometries to a single point, resulting in significant loss of spatial information. To address this limitation, we propose Omni, a geospatial ER model featuring an omni‐geometry encoder. This encoder is capable of embedding point, line, polyline, polygon, and multi‐polygon geometries, enabling the model to capture the complex geospatial intricacies of the places being compared. Furthermore, Omni leverages transformer‐based pre‐trained language models over individual textual attributes of place records in an Attribute Affinity mechanism. The model is rigorously tested on existing point‐only datasets and a new diverse‐geometry geospatial ER dataset. Omni produces up to 12% (F1) improvement over existing methods. Furthermore, we test the potential of Large Language Models (LLMs) to conduct geospatial ER, experimenting with prompting strategies and learning scenarios, comparing the results of pre‐trained language model‐based methods with LLMs. Results indicate that LLMs show competitive results.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	1361-1682
Date of First Compliant Deposit:	10 February 2026
Date of Acceptance:	14 January 2026
Last Modified:	10 Feb 2026 11:46
URI:	https://orca.cardiff.ac.uk/id/eprint/184609

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