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Automatic building semantic segmentation is the most critical and relevant task in several geospatial applications. Methods based on convolutional neural networks (CNNs) are mainly used in current building segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve the semantic segmentation of building by CNNs. In this paper, we propose a novel weakly supervised framework for building segmentation, which generates high-quality pixel-level annotations and optimizes the segmentation network. A superpixel segmentation algorithm can predict a boundary map for training images. Then, Superpixels-CRF built on the superpixel regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, we can train a more robust segmentation network and predict segmentation maps. To iteratively optimize the segmentation network, the predicted segmentation maps are refined, and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework achieves a marked improvement in the building’s segmentation quality while reducing human labeling efforts.

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The segmentation of building from satellite and airborne images is necessary for highresolution buildings maps generation and it is still challenging. On annotated pixel-level images, trained deep convolutional neural networks (CNNs) were used to improve segmentation of building. The cost of labelling training data is high, which reduces their usage. Human labelling efforts can be significantly reduced using weakly supervised segmentation techniques. Here, a novel weakly supervised framework is introduced for building semantic segmenting that relies on deep seeds to construct a superpixels-CRF model over superpixels segmentation in order to generate high-quality initial pixel-level annotations, as the initialization step. Then, the segmentation network is trained using the initial pixel-level annotations. Next, the CRF model is used to refine the segmentation masks, and the segmentation network is retrained to achieve accurate pixel-level annotations while iteratively optimizing the segmentation. The experimental results on three public building datasets demonstrate that the proposed framework significantly improved the quality of building semantic segmentation while remaining computationally efficient.