Feature and Output Consistency Training for Semi-Supervised Building Footprint Generation

Building footprint maps are important to urban planning and monitoring. However, most existing approaches that fall back on convolutional neural networks (CNNs), require massive annotated samples for network learning. In this research, we propose a novel semi-supervised network, which can help to deal with this issue by leveraging a large amount of unlabeled data. Considering that rich information is also encoded in feature maps, we propose to integrate the consistency of both features and outputs in the end-to-end network training of unlabeled samples on data perturbation, enabling to impose additional constraints. Experiments are conducted on Inria dataset. Our approach is much superior to the state-of-the-art methods in both quantitative and qualitative results.