Building Footprint Extraction for Large-Scale Basemaps Using Very-High-Resolution Satellite Imagery

Accurate building footprint is a fundamental element of large-scale base maps, which serve as critical inputs for urban planning, infrastructure development, environmental monitoring, and disaster management. While building footprint extraction and geometric regularization have been widely studied, their combined application for automated, large-scale basemap generation using very-high-resolution satellite imagery has received limited attention. To address this gap, this study proposes an integrated framework that leverages deep learning and geometric regularization to efficiently extract and refine building footprints for large-scale base maps. The framework first enhances spectral, spatial, and textural features of very-high-resolution satellite imagery through pan-sharpening, NDVI computation, GLCM-based texture analysis, and PCA. A Mask R-CNN model is then trained on multi-band imagery to segment building footprints, followed by geometric regularization to simplify and align polygons along dominant structural orientations. Object-based evaluation on ground-truth buildings demonstrates high performance, with 97.6% precision, 91.6% recall, and a 94.5% F1-score. The proposed systematic framework substantially reduces production time compared to manual stereo-plotting, requiring less than an hour per 5.29 km2 map sheet in operational production, representing a more than 35-fold efficiency gain. While minor geometric inaccuracies and merged adjacent buildings persist, the methodology offers a robust, scalable, and efficient approach to support large-scale base map production.