LAUSR

Buildings serve as the main places of human activities, and it is essential to automatically extract each building instance for a wide range of applications. Recently, automatic building segmentation approaches have made great progress in both detection and segmentation accuracy due to the rapid development of deep learning. However, these approaches struggle to delineate regular and accurate building boundaries due to the limitations in inferring overall structure of the building instance; this might lead to inconsistency in building geometry and difficulty in being applied directly to practical engineering. To tackle this challenge, this article presents an adaptive polygon generation algorithm (APGA), a novel method that aims at directly generating a polygonal output, parameterized as a sequence of building vertices, to outline each building instance. To achieve this, APGA predicts the candidate locations of building vertices and determines the arrangement of these vertices with the help of the position and orientation of the building boundary. Moreover, to introduce local context features and achieve improved performance of the predicted building polygon, APGA integrates finer structures around the candidate vertices to refine their positions. Experiments on several challenging building extraction datasets demonstrated that APGA outperformed state-of-the-art methods in terms of building coverage and geometric similarity.