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A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

View ORCID ProfileBen. G. Weinstein, Sarah J. Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie A. Bohlman, Ethan P. White
doi: https://doi.org/10.1101/2020.11.16.385088
Ben. G. Weinstein
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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  • For correspondence: benweinstein2010@gmail.com
Sarah J. Graves
2Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Sergio Marconi
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Aditya Singh
4Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, USA
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Alina Zare
5Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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Dylan Stewart
5Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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Stephanie A. Bohlman
3School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA
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Ethan P. White
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Abstract

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/weecology/NeonTreeEvaluation

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 17, 2020.
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A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
Ben. G. Weinstein, Sarah J. Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie A. Bohlman, Ethan P. White
bioRxiv 2020.11.16.385088; doi: https://doi.org/10.1101/2020.11.16.385088
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A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
Ben. G. Weinstein, Sarah J. Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie A. Bohlman, Ethan P. White
bioRxiv 2020.11.16.385088; doi: https://doi.org/10.1101/2020.11.16.385088

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