<italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow
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 prom...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11045930/ |
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| author | Lasith Niroshan James D. Carswell |
| author_facet | Lasith Niroshan James D. Carswell |
| author_sort | Lasith Niroshan |
| collection | DOAJ |
| description | 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. <italic>DeepMapper</italic> 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. |
| format | Article |
| id | doaj-art-1822554dec66449f997d4e527031f83f |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-1822554dec66449f997d4e527031f83f2025-08-20T03:28:44ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118161621617510.1109/JSTARS.2025.358149911045930<italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping WorkflowLasith Niroshan0https://orcid.org/0000-0002-9868-8338James D. Carswell1https://orcid.org/0000-0002-4766-7297University College Dublin, Dublin, IrelandTechnological University Dublin, Dublin, IrelandAI-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. <italic>DeepMapper</italic> 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.https://ieeexplore.ieee.org/document/11045930/Automated mappinggeospatial artificial intelligence (GeoAI)OpenStreetMapvolunteered geographic information (VGI) |
| spellingShingle | Lasith Niroshan James D. Carswell <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Automated mapping geospatial artificial intelligence (GeoAI) OpenStreetMap volunteered geographic information (VGI) |
| title | <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow |
| title_full | <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow |
| title_fullStr | <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow |
| title_full_unstemmed | <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow |
| title_short | <italic>DeepMapper:</italic> A GeoAI Approach to Automate the VGI Mapping Workflow |
| title_sort | italic deepmapper italic a geoai approach to automate the vgi mapping workflow |
| topic | Automated mapping geospatial artificial intelligence (GeoAI) OpenStreetMap volunteered geographic information (VGI) |
| url | https://ieeexplore.ieee.org/document/11045930/ |
| work_keys_str_mv | AT lasithniroshan italicdeepmapperitalicageoaiapproachtoautomatethevgimappingworkflow AT jamesdcarswell italicdeepmapperitalicageoaiapproachtoautomatethevgimappingworkflow |