This article suggests a new approach to automatic building footprint modeling using exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of the building point cloud using the bias of the Z‐coordinate histogram. This operation aims to detect the points of roof class from the building point cloud. Hence, eight rules for histogram interpretation are suggested. The second part of the suggested approach is the roof modeling algorithm. It starts by detecting the roof planes and calculating their adjacency matrix. Hence, the roof plane boundaries are classified into four categories: (1) outer boundary; (2) inner plane boundaries; (3) roof detail boundaries; and (4) boundaries related to the missing planes. Finally, the junction relationships of roof plane boundaries are analyzed for detecting the roof vertices. With regard to the resulting accuracy quantification, the average values of the correctness and the completeness indices are employed in both approaches. In the filtering algorithm, their values are respectively equal to 97.5 and 98.6%, whereas they are equal to 94.0 and 94.0% in the modeling approach. These results reflect the high efficacy of the suggested approach.

中文翻译：

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机载激光扫描建筑物点云的自动过滤和二维建模

本文提出了一种仅使用机载LiDAR数据进行自动建筑足迹建模的新方法。建议方法的第一部分是使用Z的偏差对建筑点云进行过滤坐标直方图。此操作旨在从建筑物点云中检测屋顶等级的点。因此，提出了八种直方图解释规则。建议方法的第二部分是屋顶建模算法。首先从检测屋顶平面并计算其邻接矩阵开始。因此，屋顶平面边界可分为四类：（1）外边界；（2）内平面边界；（3）屋顶细节边界；（4）与缺失平面有关的边界。最后，分析了屋顶平面边界的连接关系，以检测屋顶顶点。关于所得的精度量化，在两种方法中均采用正确性和完整性指数的平均值。在过滤算法中，它们的值分别等于97。5和98.6％，而在建模方法中它们分别等于94.0和94.0％。这些结果反映了所建议方法的高效性。