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.

中文翻译：

---

一种基于多等值线点分类过程构建等值线推理拟合的算法


从 True Digital Ortho Maps 中提取建筑物时，经常会遇到遮挡和错误识别的情况，因此很难获得完整、规则和准确的建筑物等值线。为了解决这个问题，我们开发了一种基于 Segment Anything 模型的建筑识别过程，并提出了一种用于建筑轮廓推理和拟合的新型正则化算法，该算法量化了等高线点的置信度，以从包含大量噪声的数据中准确拟合建筑物等高线，并将拟合问题重新定义为由等高线简化组成的渐进节点分类任务， 迭代正则化和合理性评估。在实验评估中，所提出的轮廓拟合算法实现了 97.99% 的交并比 （IoU），与标准轮廓边缘计数的 95.39% 一致性，以及 88.06% 的 Hausdorff 距离小于或等于 15 像素 （30 cm） 的情况，明显优于比较方法。值得注意的是，与原始等值线相比，它是唯一一种提高了 IoU （1.03%） 的等值线正则化算法。实验结果表明，所提算法有效抑制了噪声并推断出不完整的建筑物轮廓，产生了与人工划定相当的准确和规则的轮廓。它特别适用于具有近正交结构的建筑物，表现出显着的实际适用性和泛化潜力。