Testing the Quality of GeoAI-Generated Data for VGI Mapping

Dear Colleagues,

Geospatial Artificial Intelligence (GeoAI) trains models on large spatial datasets to discover patterns and make predictions for various purposes. For example, it can use these models to generate, update, and analyse geographic information, fundamentally reshaping how maps are produced, maintained, and consumed. Compared to traditional (still largely manual) methods, GeoAI-generated data can quickly create detailed maps of built environments (e.g., roads, buildings), which are essential for urban planning, disaster management, environmental monitoring, and many other location-based services and downstream applications.

While several prestigious GIScience journals have recently published Special Issues to highlight GeoAI’s remarkable potential as a tool for automated mapping, none have explicitly focused on the rising problem of “AI slop” in this domain. To address this problem, this Special Issue presents the latest research findings that assess the quality of contemporary GeoAI model outputs in terms of map feature accuracy and reliability, with the goal of maintaining VGI maps. AI data quality/reliability is a concern to all VGI/crowd-source mappers. For example, the OpenStreetMap community follows a “code of conduct” to control the uploading of automated edits into the live OSM database; this is a well-known policy to both VGI practitioners and GIScience researchers alike.

In this context, GeoAI data quality refers to both the accuracy and reliability of model-predicted map features. Where accuracy relates to its real-world position, orientation, and shape - to answer the question: Is GeoAI ready to take over? While reliability is more subjective, referring to a model’s consistency/trustworthiness, e.g., does it pass the eye-test - to answer the question: Are we ready to let it?

Previous studies published in IJGI and elsewhere have demonstrated that an accuracy/consistency gap exists between VGI/crowd-source map features and various authoritative “ground truth” spatial datasets, and that modern GeoAI solutions could potentially bridge this gap.

To explore this claim, this IJGI Special Issue is especially interested in empirical GeoAI studies from GIScience researchers around the world to attest the quality, in terms of accuracy and reliability, of their model outputs (map feature predictions) when compared directly to either/both authoritative (e.g., national mapping agency) “ground truth” data and/or current VGI maps (e.g., OSM) within their respective regions. The aim is to inform today’s VGI mapping community of the current state-of-the-art regarding GeoAI accuracy and inherent reliability, or not, for keeping crowd-source maps up-to-date.

• Applications of GeoAI to online mapping;
• Use cases integrating GeoAI data into VGI maps;
• Empirical accuracy and reliability evaluations of GeoAI map feature predictions;
• Benchmarking frameworks for GeoAI;
• Investigations of QA metrics for GeoAI-generated data;
• Investigations of QA metrics for crowd-source data;
• Future directions/challenges of GeoAI for mapping;

Dr. James D. Carswell
Dr. Lasith Niroshan
Guest Editors

Manuscript Submission Information

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• GeoAI
• segmentation
• deep learning
• automated mapping
• OpenStreetMap (OSM)
• volunteered geographic information (VGI)