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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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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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