Edge-Attentive Dual-Branch Frame Field Network for High-Precision Building Polygon Extraction

Efficient extraction of building footprints from aerial and satellite imagery is essential for urban planning, infrastructure management, and large-scale geospatial analysis. Traditional raster-based approaches provide limited geometric precision, while existing polygon-generation methods often rely on detecting and ordering small-scale building vertices, which can lead to incomplete structures, distorted shapes, and high computational cost. To address these limitations, this study proposes an Edge-Attentive Dual-Branch Frame Field Network (EA-DBFFN) for automated and high-precision building polygon extraction. The method is built upon frame field learning and introduces a dual-branch architecture that separately predicts building masks and edges. A Dual-Task Decoder enlarges and adapts receptive fields while applying spatial attention to enhance the representation of structural details. Fixed Sobel and Laplacian filters are incorporated to strengthen boundary detection. In addition, a Dual-Task Mutual Guidance Module promotes the exchange of complementary information between the mask and edge branches, improving geometric consistency and reducing boundary errors. Experiments conducted on the Inria Aerial dataset and the CrowdAI dataset demonstrate that EA-DBFFN achieves superior performance in region-based metrics, with an AP75 of 72.9% on CrowdAI, representing a 2.3% improvement over competing methods. Furthermore, EA-DBFFN produces geometrically higher-quality polygons, with the Max Tangent Angle error reduced by 6.4%, the Invalid Polygon Ratio reduced by 66.3%, and Edge Smoothness improved by 72.7% compared to the best competing method. The results show that EA-DBFFN provides an effective and computationally efficient framework for generating high-quality vectorized building footprints suitable for large-scale urban analysis.