论文标题
DALES：用于语义分割的大型空中激光雷达数据集
DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
论文作者
论文摘要
我们介绍了Dayton注释的LiDAR Earth Scan（DALES）数据集，这是一种新的大规模空中LIDAR数据集，其横跨10平方公里的面积和八个物体类别的手工标记点以上。大量注释的点云数据集已成为评估深度学习方法的标准。但是，大多数现有数据集都集中在从移动或地面扫描仪中收集的数据上，很少关注航空数据。从空中激光扫描仪（ALS）收集的点云数据在3D城市建模和大规模监视等领域提出了一套新的挑战和应用。 Dales是最广泛的公共ALS数据集，其点数超过400倍，是目前可用的带注释的空中点云数据集的分辨率的六倍。该数据集为评估新的3D深度学习算法评估的专家验证的手工标记点提供了关键数量，有助于将当前算法的重点扩展到航空数据上。我们描述了数据的性质，注释工作流程，并提供了DALES数据集中当前最新算法性能的基准。
We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.
