🤖 AI Summary
This study addresses the prevalence of topological errors in building footprints generated by deep learning models, which hinder their direct integration into GIS databases. To tackle this issue, the authors propose a multi-domain GeoAI quality control framework that fuses 24-dimensional features encompassing geometric, spatial contextual, and spectral-textural attributes. By integrating geometric regularization and spatial mutual exclusion constraints, the framework enables object-level automated quality inspection and purification. Initial masks are produced using U-Net (ResNet-34) and SAM-LoRA (ViT-B), followed by boundary deformation and duplicate object detection via decision tree classifiers. Experimental results demonstrate that the framework achieves 95.31% accuracy, 91.06% F1-score, and 0.880 Matthews correlation coefficient on an independent test area, with an 87.34% error footprint detection rate. This approach significantly enhances building database purity to 95.38% and reduces relative error by 83.09%, offering a robust and transferable quality assurance mechanism for automated GIS production.
📝 Abstract
Deep learning-based building footprint extraction from high-resolution imagery often produces topologically inconsistent vectors unfit for direct GIS database ingestion. To address this, we present a multidomain GeoAI quality control framework that automates error detection to systematically purify vector footprint databases. Candidate footprints were generated across five UAV survey sites in Bangladesh using U-Net (ResNet-34) and SAM-LoRA (ViT-B). The extracted raster masks were vectorized, geometrically regularized, and consolidated under a spatial-exclusivity constraint to eliminate duplicate representations. We used twenty-four predictors capturing geometric, spatial-contextual, and raster-derived spectral and texture properties. Machine Learning (ML) classifiers were trained on a development partition (Sites B-D) and rigorously validated on a spatially independent test set (Site E) excluded from hyperparameter tuning and class balancing. The experimental results demonstrate that geometric and spatial-contextual predictors using Decision Tree (DT) provide the most effective discriminatory evidence for identifying object-level boundary deformations. DT achieved an accuracy of 95.31%, an F1-score of 91.06%, and a Matthews correlation coefficient (MCC) of 0.880 on the unseen testing site. At the database level, this framework successfully identified 87.34% of erroneous footprints while maintaining 98.31% of acceptable structures, reducing the residual error proportion from 27.32% to 4.62% and improving final database purity to 95.38%. This translates into a relative error reduction of 83.09%. The findings indicate that post-segmentation object-level ML provides a highly transferable, robust mechanism for automated quality assurance in production-ready geographic information system (GIS) workflows.