Since the launch of Landsat-1 in 1972, Earth observation satellites have undergone significant advancements, enabling the collection of vast amounts of high-resolution imagery. These satellites continuously provide critical data for monitoring urban expansion, infrastructure development, and disaster response. In recent years, the number of remote sensing satellites in orbit has increased substantially, generating extensive visual datasets essential for precise spatial mapping across civil, public, and military applications. One of the key challenges in utilizing satellite imagery is the automated reconstruction of building footprints, which demands high precision to account for variations in architectural styles. Traditional methods rely on manual or semi-automated approaches, which are often time-consuming and prone to inaccuracies. To address these limitations, this paper introduces PolyAttractNet, a novel deep learning framework designed to improve building boundary delineation in satellite imagery. Our approach incorporates Attraction Field Maps (AFMs) within a Graph Neural Network (GNN) framework, combined with an enhanced Mask R-CNN backbone. The proposed architecture effectively detects building instances from a single satellite image while minimizing boundary noise by embedding geometric regularity and integrating multi-scale, multi-resolution, and boundary-preserving mask features. AFMs play a crucial role in refining boundary precision by guiding feature extraction toward geometric consistency. As a result, our model achieves a 9.6% improvement in Average Precision (AP) and a 5% increase in Average Recall (AR) compared to the baseline, demonstrating its effectiveness in producing more accurate and regularized building footprints.