Automatic alignment- and correction-based building labeling method for intelligent dataset updating

A large-capacity and high-quality building extraction dataset is the basis for intelligently extracting buildings from large-scale heterogeneous images. A deep learning-based automatic building alignment and correction model is proposed to address the difficulty of updating building extraction dataset derived from remote sensing images. An intelligent update method for the building extraction dataset is implemented based on this model. The model first uses a multiscale fully convolutional encoder-decoder architecture to match building instances between map data and remote sensing images and obtains classification labels, including similar, redundant, and missing labels; then, the model implements pairing via an instance transformation network. A spatial transformation is performed on similar labels to obtain aligned building labeling results. Finally, the redundant labels are deleted, the missing labels obtained from the segmentation process are regularized to achieve instance optimization, and corrected building labeling results are obtained. Experiments show that the automatic alignment and correction method proposed in this paper for building labeling can effectively improve the accuracy of building labeling and can significantly improve the efficiency of dataset updates while satisfying dataset quality requirements.