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
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.16578

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 from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods' ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46-0.55, macro F1 = 0.09-0.32, cross entropy loss = 2.4-9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07-0.32, macro F1 = 0.02-0.18, cross entropy loss = 2.8-16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.