Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers
Not on the map. Why?
Abstract (arXiv)
A rich amount of geographic information exists in unstructured texts, such as Web pages, social media posts, housing advertisements, and historical archives. Geoparsers are useful tools that extract structured geographic information from unstructured texts, thereby enabling spatial analysis on textual data. While a number of geoparsers were developed, they were tested on different datasets using different metrics. Consequently, it is difficult to compare existing geoparsers or to compare a new geoparser with existing ones. In recent years, researchers created open and annotated corpora for testing geoparsers. While these corpora are extremely valuable, much effort is still needed for a researcher to prepare these datasets and deploy geoparsers for comparative experiments. This paper presents EUPEG: an Extensible and Unified Platform for Evaluating Geoparsers. EUPEG is an open source and Web based benchmarking platform which hosts a majority of open corpora, geoparsers, and performance metrics reported in the literature. It enables direct comparison of the hosted geoparsers, and a new geoparser can be connected to EUPEG and compared with other geoparsers. The main objective of EUPEG is to reduce the time and effort that researchers have to spend in preparing datasets and baselines, thereby increasing the efficiency and effectiveness of comparative experiments.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 9 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 3; 2026: 2.
9 papers.
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
- 2023GeoLM: Empowering Language Models for Geospatially Grounded Language Understandingcited by 37
- 2022Location reference recognition from texts: A survey and comparisoncited by 17
- 2024Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Inputcited by 4
- 2025Urban Computing in the Era of Large Language Modelscited by 2
- 2026Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in Germancited by 2
- 2026Assessing socio-economic climate impacts from text datacited by 1
- 2025Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencingcited by 0
- 2025Coordinates from Context: Using LLMs to Ground Complex Location Referencescited by 0
- 2026Assessing socio-economic climate impacts from text data20 May 2026
- 2026Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German5 May 2026
- 2025Coordinates from Context: Using LLMs to Ground Complex Location References9 Oct 2025
- 2025Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencing11 Jul 2025
- 2025Urban Computing in the Era of Large Language Models2 Apr 2025
- 2024Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input29 Apr 2024
- 2023GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding23 Oct 2023
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence13 Apr 2023
- 2022Location reference recognition from texts: A survey and comparison4 Jul 2022