RT Journal Article
SR Electronic
T1 A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.16.385088
DO 10.1101/2020.11.16.385088
A1 Ben. G. Weinstein
A1 Sarah J. Graves
A1 Sergio Marconi
A1 Aditya Singh
A1 Alina Zare
A1 Dylan Stewart
A1 Stephanie A. Bohlman
A1 Ethan P. White
YR 2020
UL http://biorxiv.org/content/early/2020/11/17/2020.11.16.385088.abstract
AB 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 StatementThe authors have declared no competing interest.