Data science competition for cross-site individual tree species identification from airborne remote sensing data

• Published in: PeerJ (San Francisco, CA) Vol. 11; p. e16578
• Main Authors: Graves, Sarah J, Marconi, Sergio, Stewart, Dylan, Harmon, Ira, Weinstein, Ben, Kanazawa, Yuzi, Scholl, Victoria M, Joseph, Maxwell B, McGlinchy, Joseph, Browne, Luke, Sullivan, Megan K, Estrada-Villegas, Sergio, Wang, Daisy Zhe, Singh, Aditya, Bohlman, Stephanie, Zare, Alina, White, Ethan P
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 21.12.2023; PeerJ, Inc; PeerJ Inc
• Subjects: Airborne remote sensing; Biodiversity; Classification; Competition; Computational Science; Data Science; Data science competition; Datasets; Deep learning; Ecology; Ecosystem; Forest ecology; Forest management; Forestry; Humans; National ecological observatory network; Neural networks; Neural Networks, Computer; Observatories; Remote sensing; Remote Sensing Technology; Spatial and Geographic Information Science; Species; Species classification; Taxonomy; Trees; United States > US; Airborne remote sensing; Species classification; National ecological observatory network; Data science competition

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions fr...