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Jahangirnagar University

Academic institutionasia · bd
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Representative Papers

GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering

Aug 09, 2026

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.

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GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering

Aug 09, 2026

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.

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