DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar

被引:0
|
作者
Singer, Nina M. [1 ]
Asari, Vijayan K. [1 ]
机构
[1] Univ Dayton, Dept Elect & Comp Engn, Dayton, OH 45469 USA
来源
IEEE ACCESS | 2021年 / 9卷
关键词
Three-dimensional displays; Semantics; Benchmark testing; Laser radar; Task analysis; Deep learning; Vegetation mapping; 3D data set; aerial vision; airborne system; ALS; benchmark data; data annotation; deep learning; earth scan; instance segmentation; laser scan; lidar; point cloud; semantic segmentation; CLASSIFICATION;
D O I
10.1109/ACCESS.2021.3094127
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.
引用
收藏
页码:97495 / 97504
页数:10
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