PERFORMANCE EVALUATION OF FUSION TECHNIQUES FOR CROSS-DOMAIN BUILDING ROOFTOP SEGMENTATION
Keywords: Building roof segmentation, Neural network, Self-training, HRNet, OCRNet, Swin Transfomer
Abstract. Convolutional Neural Networks have been widely introduced to building rooftop segmentation using satellite and aerial imagery. Preparing efficient training data is still among the critical issues on this topic. Therefore, adopting available annotated cross-domain multisource dataset is needed. This paper evaluates the performance of fusing the state-of-art deep learning neural network architectures for cross-domain building rooftop segmentation. We have selected three semantic image segmentation neural networks, including Swin transformer, OCRNet and HRNet. The predictions from these three neural networks are combined with majority voting, max value and union fusion techniques, a refined building rooftop segmentation mask is therefore delivered. The experiments on two benchmark datasets show that the proposed fusion techniques outperform single models and other state-of-art cross-domain segmentation approaches.