2026-09-10T09:45:01Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27555 2026-09-10T05:38:42Z ddc:004 open_access

Georeferencing Non-Gazetteered Place Names Using Biological Specimen Records Fernando, Aneesha Ranathunga, Surangika Stock, Kristin Prasanna, Raj Jones, Christopher B. Non-gazetteered Place Names Gazetteers Toponyms Georeferencing Spatial Relation Modelling Large Language Models Biological specimen records collected by natural history institutions constitute a rich source of temporal geographic knowledge, capturing biodiversity information about regional landscapes as they were recorded at different times. Using digitised data from the Allan Herbarium (New Zealand), this study identifies place names in these specimen locality descriptions that are absent from current gazetteers; we refer to these as non-gazetteer place names (NGPs). These place names are typically historical, vernacular, or colloquial and were used as landmarks to describe a specimen’s location at the time of collection. We then investigate the problem of georeferencing the NGPs using only the limited information available in the specimen records. To resolve this, we leverage repeated occurrences of the same place name across specimen records with different specimen locations and spatial relation terms, extracting and inverting these relations to derive constraints on NGP locations. This approach is instantiated within deterministic, probabilistic, and LLM-based methods, enabling a comparative analysis of their strengths and limitations for text-based spatial inference. On a pseudo-NGP benchmark, probabilistic inference achieves the highest accuracy (median error 1.43 km; A@1 km 36%), while the LLM yields competitive but less precise estimates (median error 1.80 km; A@1 km 31%), indicating that, despite advances in LLMs, traditional modelling remains advantageous when high spatial precision is required. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Aneesha Fernando and Surangika Ranathunga and Kristin Stock and Raj Prasanna and Christopher B. Jones 2026 Is Part Of LIPIcs, Volume 393, 17th International Conference on Spatial Information Theory (COSIT 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.COSIT.2026.11 urn:nbn:de:0030-drops-275554 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2026.11 eng https://creativecommons.org/licenses/by/4.0/legalcode