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

Author(s)

Nina M. Singer (0000-0001-5916-6425), University of DaytonFollow
Vijayan K. Asari (0000-0002-3751-5492), University of DaytonFollow

Document Type

Article

Publication Date

2021

Publication Source

IEEE Access

Abstract

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.

Inclusive pages

97495-97504

ISBN/ISSN

2169-3536

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2021.3094127

Publisher

IEEE-Inst Electrical Electronics Engineers Inc

Volume

9

Peer Reviewed

yes

eCommons Citation

Singer, Nina M. and Asari, Vijayan K., "DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar" (2021). Electrical and Computer Engineering Faculty Publications. 482.
https://ecommons.udayton.edu/ece_fac_pub/482