PolyRoof: Precision Roof Polygonization in Urban Residential Building with Graph Neural Networks
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
The growing demand for detailed building roof data has driven the development
of automated extraction methods to overcome the inefficiencies of traditional
approaches, particularly in handling complex variations in building geometries.
Re:PolyWorld, which integrates point detection with graph neural networks,
presents a promising solution for reconstructing high-detail building roof
vector data. This study enhances Re:PolyWorld's performance on complex urban
residential structures by incorporating attention-based backbones and
additional area segmentation loss. Despite dataset limitations, our experiments
demonstrated improvements in point position accuracy (1.33 pixels) and line
distance accuracy (14.39 pixels), along with a notable increase in the
reconstruction score to 91.99%. These findings highlight the potential of
advanced neural network architectures in addressing the challenges of complex
urban residential geometries.
