Human–AI collaboration in volunteered geographic information: a performance assessment of the fAIr mapping environment

The growing use of geographic artificial intelligence (GeoAI) in volunteered geographic information is reshaping Digital Earth workflows through collaboration between human contributors and machine-learning models. OpenStreetMap is central to this ecosystem, yet evidence on how AI-assisted editors affect mapper behavior, data quality, and mapping performance remains limited. We evaluate fAIr, an open, locally trainable building-mapping environment developed by the Humanitarian OpenStreetMap Team. In a controlled experiment involving 26 participants, we compared fAIr with JOSM, a widely used editor for manual mapping. Across simple and complex study areas, we assessed mapping efficiency and effectiveness, classified error types, and examined participants’ adjustments to model parameters. Manual mapping in JOSM was faster and more accurate overall, mainly because experienced contributors performed well. fAIr reduced differences between novice and experienced contributors but produced AI-related errors, most commonly the merging of multiple buildings into a single footprint. Participants adjusted only some of fAIr’s parameters. Overall, fAIr offers a fast, accessible workflow for mapping simple structures, but its broader application depends on reducing recurring errors in building-footprint prediction. These findings informed the development of a new version of fAIr.