DeepMapper: A GeoAI Approach to Automate the VGI Mapping Workflow
ORCID
- James D. Carswell: 0000-0002-4766-7297
Abstract
AI-powered mapping technologies are uniquely positioned to address the challenges of keeping online maps up-to-date in dynamic urban environments. Modern geospatial artificial intelligence (GeoAI) approaches, particularly those utilizing advanced deep learning techniques, have shown significant promise in automating aspects of the volunteered geographic information (VGI) map updating process. This article introduces DeepMapper, an open-source, GeoAI-based mapping platform designed to automate the VGI mapping workflow with minimal human intervention. By applying generative adversarial networks to freely available spatial datasets, such as OpenStreetMap, DeepMapper enhances the accuracy and completeness of VGI maps. DeepMapper is a web-based mapping tool that streamlines/automates much of the traditional VGI map updating workflow. Experiments demonstrate that DeepMapper achieves an accuracy of 92.8%, precision of 90.3%, and recall of 92.0%. The platform also shows potential for testing new GeoAI models, facilitating continuous improvements in how contemporary VGI maps are created and maintained. Despite its successes, challenges remain, particularly in adapting DeepMapper to real-world settings and different architectural styles. Further work will explore the integration of diffusion models for building detection and address nontechnical challenges related to geometric accuracy and the adoption of AI-based mapping by communities, such as OpenStreetMap.
Keywords
Automated mapping, geospatial artificial intelligence (GeoAI), OpenStreetMap, volunteered geographic information (VGI)
DOI Link
Publication Date
2025-01-01
Publication Title
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume
18
ISSN
1939-1404
First Page
16162
Last Page
16175
Deposit Date
2026-10-22
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Niroshan, L., & Carswell, J. (2025) 'DeepMapper: A GeoAI Approach to Automate the VGI Mapping Workflow', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, pp. 16162-16175. Available at: 10.1109/JSTARS.2025.3581499