A Human-in-the-Loop GeoAI–GIS Workflow for Structured Spatial Evidence in Sustainable Cultivated-Land Governance

Sustainable cultivated-land governance requires spatial information that is accurate, traceable, and suitable for expert review. This study developed a human-in-the-loop GeoAI–GIS workflow that transforms high-resolution semantic segmentation outputs into image-derived land-cover objects, transparent review priorities, and structured spatial evidence records. The Cultivated Land Information Management Transformer (CLIM-Former), trained on LoveDA and transferred to Gaofen-2 imagery of Mancheng District without target-area fine-tuning, provided the semantic basis for downstream object construction. Connected-component analysis generated measurable objects with semantic, geometric, locational, contextual, and provenance attributes, while interpretable rules prioritized objects for review and deterministic templates produced standardized records and verification case sheets. Across three training seeds, the guard-enabled downstream configuration achieved a mean mIoU of 56.27 ± 0.10% and an agriculture IoU of 63.12 ± 0.90%. In a reproducible 100-tile subset, 3352 objects were retained, and 1705 were prioritized. Expert acceptance was 83.00% for 100 sampled prioritized objects and 37.00% for 100 non-prioritized controls, corresponding to a 2.24-fold enrichment with a 95% confidence interval of 1.71–2.94 and a significant between-group difference according to Fisher’s exact test at p < 0.001. The generated records achieved 100% key information coverage, 95.05% factual consistency, and a mean suitability score of 4.28/5. The workflow provides a transparent and reviewable link between remote-sensing interpretation and cultivated-land information management while preserving expert authority over management decisions.