Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise deep learning method Dynamic Graph Convolutional Neural Network (DGCNN) and extend its classification application from indoor scenes to airborne point clouds. This study proposes an approach to provide cheap training samples for point-wise deep learning using an existing 2D base map. Furthermore, essential features and spatial contexts to effectively classify airborne point clouds colored by an orthophoto are also investigated, in particularly to deal with class imbalance and relief displacement in urban areas. Two airborne point cloud datasets of different areas are used: Area-1 (city of Surabaya—Indonesia) and Area-2 (cities of Utrecht and Delft—the Netherlands). Area-1 is used to investigate different input feature combinations and loss functions. The point-wise classification for four classes achieves a remarkable result with 91.8% overall accuracy when using the full combination of spectral color and LiDAR features. For Area-2, different block size settings (30, 50, and 70 m) are investigated. It is found that using an appropriate block size of, in this case, 50 m helps to improve the classification until 93% overall accuracy but does not necessarily ensure better classification results for each class. Based on the experiments on both areas, we conclude that using DGCNN with proper settings is able to provide results close to production.

中文翻译：

---

使用DGCNN深度学习方法的机载激光扫描点云分类

高精度的空中点云分类对于许多地理应用而言意义重大，但由于数据量大且结构无序，因此并非易事。近年来，针对3D点云分类的深度学习已得到积极开发和应用，尤其是在室内场景中。在这项研究中，我们实现了点式深度学习方法动态图卷积神经网络（DGCNN），并将其分类应用程序从室内场景扩展到了空中点云。这项研究提出了一种使用现有2D基本地图为点式深度学习提供廉价训练样本的方法。此外，还研究了有效分类由正射影像着色的空中点云的基本特征和空间环境，特别是要解决城市地区的阶级失衡和救济流离失所问题。使用了两个不同区域的空中点云数据集：Area-1（泗水市—印度尼西亚）和Area-2（乌特勒支市和代尔夫特市—荷兰）。Area-1用于研究不同的输入要素组合和损失函数。使用光谱颜色和LiDAR功能的全部组合时，四个类别的逐点分类可实现卓越的结果，总体精度为91.8％。对于Area-2，研究了不同的块大小设置（30、50和70 m）。发现在这种情况下，使用适当的块大小（50 m）有助于改善分类，直到总精度达到93％为止，但不一定确保每个分类的分类结果更好。根据这两个领域的实验，