BUILDING EXTRACTION BY GATED GRAPH CONVOLUTIONAL NEURAL NETWORK WITH DEEP STRUCTURED FEATURE EMBEDDING

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 we introduce a generic framework to overcome the issue, integrating the gated graph convolutional network (GGCN) and deep structured feature embedding (DSFE) into an end-to-end work-flow.