An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively.

中文翻译：

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使用DSM和图像信息进行建筑物检测和规范化

提出了一种自动进行建筑轮廓正则化的方法，该方法结合了数据驱动和模型驱动方法来提供可靠的解决方案。该方法的核心部分包括一种新颖的数据驱动方法，可从给定边界点的列表中生成近似的建筑多边形。该算法使用从建筑物的几何特性派生的函数迭代计算并存储任意起始边界点和随后的每个边界点之间的似然值。作为预处理步骤，必须使用用于提取数字高程模型的鲁棒算法来识别建筑物段。具有挑战性的数据集的评估结果的平均正确率分别为96·3 ％和95·7 ％ 分别用于建筑物检测和规范化。