An algorithm for building contour inference fitting based on multiple contour point classification processes

Abstract

Extracting buildings from True Digital Ortho Maps often encounters occlusions and misidentifications, making it challenging to obtain complete, regular, and accurate building contours. To address this issue, we developed a building recognition process based on the Segment Anything Model, and proposed a novel regularization algorithm for building contour inference and fitting, which quantifies the confidence levels of contour points to accurately fit building contours from data containing substantial noise, and reformulates the fitting problem as progressive node classification tasks consisting of contour simplification, iterative regularization, and rationality assessment. In experimental evaluations, the proposed contour fitting algorithm achieved 97.99 % Intersection over Union (IoU), 95.39 % consistency with the standard contour edge count, and 88.06 % of cases with Hausdorff distances less than or equal to 15 pixels (30 cm), significantly outperforming comparative methods. Notably, it was the only contour regularization algorithm that improved IoU (1.03 %) compared to the original contours. The experimental results demonstrate that the proposed algorithm effectively suppresses noise and infers incomplete building contours, producing accurate and regular contours comparable to manual delineation. It is particularly suitable for buildings with near-orthogonal structures, exhibiting significant practical applicability and generalization potential.