The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D point
clouds, which are accessible, affordable, accurate, and applicable. Among all types of platforms, the airborne platform with LiDAR can serve as an efficient and effective tool for large-scale 3D mapping in the urban area. Against this background, a large number of algorithms and methods have been developed to fully explore the potential of 3D point clouds. However, the creation of
publicly accessible large-scale annotated datasets, which are critical for assessing the performance of the developed algorithms and methods, is still at an early age. In this work, we present a large-scale aerial LiDAR point cloud dataset acquired in a highly-dense and complex urban area for the evaluation of semantic labeling methods. This dataset covers an urban area with highly-dense buildings of approximately 1 km² and includes more than 3 million points with five classes of objects labeled. Moreover, experiments are carried out with the results from several baseline methods, demonstrating the feasibility and capability of the dataset serving as a benchmark for assessing semantic labeling methods.



中文翻译：

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LASDU：用于密集城市地区语义标记的大规模航空LiDAR数据集

对于诸如地图，导航和监视之类的各种应用，市区的语义标记是一项必不可少但具有挑战性的任务。光检测和测距（LiDAR）系统的快速发展为这项任务提供了使用3D点
云的可能解决方案，该点云可访问，可负担，准确且适用。在所有类型的平台中，具有LiDAR的机载平台都可以用作市区大规模3D映射的高效工具。在这种背景下，已经开发了大量算法和方法来充分探索3D点云的潜力。但是，创造
对于评估已开发算法和方法的性能至关重要的，可公开访问的大规模注释数据集仍处于早期阶段。在这项工作中，我们提出了在高密度和复杂的市区中获取的大规模空中LiDAR点云数据集，用于评估语义标记方法。该数据集覆盖了大约1 km ^2的高密度建筑物的市区，并包含超过300万个点，并标记了五类对象。此外，利用几种基线方法的结果进行了实验，证明了数据集作为评估语义标记方法的基准的可行性和能力。