ORCID
- James D. Carswell: 0000-0002-4766-7297
Abstract
Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data.
Keywords
GeoAI, map feature accuracy, OpenStreetMap, Quality Assurance, volunteered geographic information
DOI Link
Publication Date
2025-01-01
Publication Title
Land
Volume
14
Issue
10
Deposit Date
2026-02-24
Funding
This research is funded by Technological University Dublin College of Arts and Tourism, SEED FUNDING INITIATIVE 2019–2020.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Niroshan, Lasith and Carswell, James D., "Evaluating GeoAI-Generated Data for Maintaining VGI Maps" (2025). Research Outputs: 2025-Present. 2.
https://arrow.tudublin.ie/aaschmedro/2