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

ABSTRACT: 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

SUBMITTER: Graves SJ 

PROVIDER: S-EPMC10749090 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature