Abstract

Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a class, object or feature, to each point. This research uses ConvPoint, a deep learning method, to perform airborne point cloud classification at scale, in rural and urban contexts. Specifically, our experiments are located near Montreal (QC) and Saint-Jean (NB) and our approach is designed to classify five classes; we used “Building”, “Ground”, “Water”, “Low Vegetation” and “Mid-High Vegetation”. Experimenting with different configurations, we achieved excellent Intersection-over-Union results for the “Mid-High Vegetation” (93%) and “Building” (86%) classes on both datasets and provide insights to improve processing times as well as accuracy.

摘要

机载 LiDAR 数据允许对地形进行精确建模，并在多种情况下使用。为了便于进一步分析，点云分类过程允许为每个点分配一个类、对象或特征。本研究使用深度学习方法 ConvPoint 在农村和城市环境中大规模执行机载点云分类。具体来说，我们的实验位于蒙特利尔 (QC) 和 Saint-Jean (NB) 附近，我们的方法旨在对五个类别进行分类；我们使用了“建筑”、“地面”、“水”、“低植被”和“中高植被”。通过尝试不同的配置，我们在两个数据集上的“中高植被”(93%) 和“建筑”(86%) 类取得了出色的交集结果，并提供了改进处理时间和准确性的见解。