Building Footprint Extraction with Graph Convolutional Network

• URL: http://arxiv.org/abs/2305.04499v1
• Date: Mon, 8 May 2023 06:50:05 GMT
• Title: Building Footprint Extraction with Graph Convolutional Network
• Authors: Yilei Shi, Qinyu Li, Xiaoxiang Zhu
• Abstract summary: Building footprint information is an essential ingredient for 3-D reconstruction of urban models. Recent developments in deep convolutional neural networks (DCNNs) have enabled accurate pixel-level labeling tasks. 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.
• Score: 20.335884170850193
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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