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

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard dat...

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Detalles Bibliográficos
Autores principales: Weinstein, Ben G., Graves, Sarah J., Marconi, Sergio, Singh, Aditya, Zare, Alina, Stewart, Dylan, Bohlman, Stephanie A., White, Ethan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8282040/
https://www.ncbi.nlm.nih.gov/pubmed/34214077
http://dx.doi.org/10.1371/journal.pcbi.1009180
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author Weinstein, Ben G.
Graves, Sarah J.
Marconi, Sergio
Singh, Aditya
Zare, Alina
Stewart, Dylan
Bohlman, Stephanie A.
White, Ethan P.
author_facet Weinstein, Ben G.
Graves, Sarah J.
Marconi, Sergio
Singh, Aditya
Zare, Alina
Stewart, Dylan
Bohlman, Stephanie A.
White, Ethan P.
author_sort Weinstein, Ben G.
collection PubMed
description Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. 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, 400 field-annotated crowns, and 3,000 canopy 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 data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods.
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spelling pubmed-82820402021-07-28 A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network Weinstein, Ben G. Graves, Sarah J. Marconi, Sergio Singh, Aditya Zare, Alina Stewart, Dylan Bohlman, Stephanie A. White, Ethan P. PLoS Comput Biol Research Article Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. 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, 400 field-annotated crowns, and 3,000 canopy 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 data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods. Public Library of Science 2021-07-02 /pmc/articles/PMC8282040/ /pubmed/34214077 http://dx.doi.org/10.1371/journal.pcbi.1009180 Text en © 2021 Weinstein et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Weinstein, Ben G.
Graves, Sarah J.
Marconi, Sergio
Singh, Aditya
Zare, Alina
Stewart, Dylan
Bohlman, Stephanie A.
White, Ethan P.
A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title_full A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title_fullStr A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title_full_unstemmed A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title_short A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
title_sort benchmark dataset for canopy crown detection and delineation in co-registered airborne rgb, lidar and hyperspectral imagery from the national ecological observation network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8282040/
https://www.ncbi.nlm.nih.gov/pubmed/34214077
http://dx.doi.org/10.1371/journal.pcbi.1009180
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