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arXiv · 2305.04499

Building Footprint Extraction with Graph Convolutional Network

Yilei Shi·Qinyu Li·Xiaoxiang Zhu
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Abstract

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.

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Yilei Shi, Qinyu Li, Xiaoxiang Zhu. 2023-05-08. Building Footprint Extraction with Graph Convolutional Network. https://doi.org/10.1109/igarss.2019.8898764

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