LCS: A Collaborative Optimization Framework of Vector Extraction and Semantic Segmentation for Building Extraction

In the field of building extraction, many convolutional neural network (CNN)-based methods have been developed to solve the problem of the irregular boundaries in their predictions. The prevailing approach is to build an additional edge segmentation branch or obtain accurate vector components of buildings. However, pixel-based methods still cannot obtain an accurate location of the boundary, while vector extraction will bring the problem of sample imbalance and missing detection. In this work, we utilize the complementarity of the two types of methods and propose the line segment collaborate segmentation (LCS) framework. In the proposed LCS framework, semantic segmentation provides location guidance for vector extraction, while vector extraction provides precise positioning for semantic segmentation. In this way, the two tasks can leverage their respective strengths. The results on three datasets show that the performance of vector extraction and semantic segmentation is improved simultaneously using the LCS framework, which proves the effectiveness of our method. At the same time, our framework is flexible and can be embedded in other vector extraction methods to improve performance.