Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. Recent developments in deep convolutional neural networks (DCNNs) have enabled accurate pixel-level labeling tasks. One central issue remains, which is the precise delineation of boundaries. Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to progressive downsampling. In this work, we have proposed a end-to-end framework to overcome this issue, which uses the graph convolutional network (GCN) for building footprint extraction task. Our proposed framework outperforms state-of-the-art methods.
@article{arxiv.2305.04499,
title = {Building Footprint Extraction with Graph Convolutional Network},
author = {Yilei Shi and Qinyu Li and Xiaoxiang Zhu},
journal= {arXiv preprint arXiv:2305.04499},
year = {2023}
}
Comments
4 pages. arXiv admin note: text overlap with arXiv:1911.03165