Building Footprint Extraction with Graph Convolutional Network
Yilei Shi Qinyu Li Xiaoxiang Zhu
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.