P2PFormerV2: Improving Primitive-Based Regular Building Contour Extraction Methods via Contour Feature Enhancement

Deep learning methods have been widely used to map building contours automatically in high-resolution remote sensing images over recent years. However, most deep learning-based methods still require complex post-processing to generate regular contours. P2PFormer is an advanced method that can directly obtain the positions and sequences of general geometric primitives like points, lines, and angles (corners) of a building instance without complex post-processing. Nevertheless, due to the inherent limitations of ROI-Align, P2PFormer faces significant challenges in primitive detection. ROI-Align utilizes a uniform sampling approach for feature extraction, it inevitably introduces a high proportion of invalid sampling points into the extracted features. Resulting in missed and false detections of building primitives, ultimately affecting the accuracy of building contour extraction. To address these issues, we propose P2PFormerV2, which introduces a contour feature enhancer to improve P2PFormer. The contour feature enhancer increases the proportion of valid feature sampling points and enhances the extraction of contour-aware features, significantly improving the accuracy of primitive segmentation. This enhancer comprises three key components: the sparse feature extractor, the dense feature extractor, and the feature fusion module. The sparse feature extractor optimizes the sampling strategy to increase the proportion of valid feature sampling points; the dense feature extractor generates rich contour features and provides additional supervision signals; the feature fusion module integrates the outputs of the first two components to further enhance the extraction of contour features. Experimental results demonstrate that P2PFormerV2 achieves average precisions (AP) of 74.7%, 79.6%, and 64.2% in the WHU, CrowdAI, and WHU-Mix datasets, respectively, significantly outperforming the original P2PFormer and other existing advanced methods. Our findings about the shortcomings of ROI-Align and the importance of improving the effective feature extraction provides insights for future building extraction research.