DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
- URL: http://arxiv.org/abs/2004.11985v1
- Date: Tue, 14 Apr 2020 20:05:28 GMT
- Title: DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
- Authors: Nina Varney, Vijayan K. Asari and Quinn Graehling
- Abstract summary: 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.
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.
- Score: 8.486713415198972
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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