Transformer Based Building Boundary Reconstruction using Attraction Field Maps

TL;DR

This paper presents Decoupled-PolyGCN, a novel deep learning approach using Graph Convolutional Networks and Attraction Field Maps to improve automated building footprint reconstruction from satellite images, enhancing accuracy and regularity.

Contribution

The paper introduces Decoupled-PolyGCN, a new GCN-based method that incorporates geometric regularity and Attraction Field Maps for superior building boundary reconstruction.

Findings

Outperforms existing methods by 6% in AP

Achieves 10% higher AR

Provides scalable, accurate building footprints

Abstract

In recent years, the number of remote satellites orbiting the Earth has grown significantly, streaming vast amounts of high-resolution visual data to support diverse applications across civil, public, and military domains. Among these applications, the generation and updating of spatial maps of the built environment have become critical due to the extensive coverage and detailed imagery provided by satellites. However, reconstructing spatial maps from satellite imagery is a complex computer vision task, requiring the creation of high-level object representations, such as primitives, to accurately capture the built environment. While the past decade has witnessed remarkable advancements in object detection and representation using visual data, primitives-based object representation remains a persistent challenge in computer vision. Consequently, high-quality spatial maps often rely on labor-intensive and manual processes. This paper introduces a novel deep learning methodology leveraging Graph Convolutional Networks (GCNs) to address these challenges in building footprint reconstruction. The proposed approach enhances performance by incorporating geometric regularity into building boundaries, integrating multi-scale and multi-resolution features, and embedding Attraction Field Maps into the network. These innovations provide a scalable and precise solution for automated building footprint extraction from a single satellite image, paving the way for impactful applications in urban planning, disaster management, and large-scale spatial analysis. Our model, Decoupled-PolyGCN, outperforms existing methods by 6% in AP and 10% in AR, demonstrating its ability to deliver accurate and regularized building footprints across diverse and challenging scenarios.