Data Science Competition: Airborne Remote Sensing to Ecological Information - a PeerJ Collection

Hyperspectral remote sensing imagery and individual tree crowns from the Ordway Swisher Biological Station, part of the National Ecological Observatory Network.

The "Data Science Competition: Airborne Remote Sensing to Ecological Information" Collection contains papers and preprints describing the results of the National Institute of Standards and Technology (NIST) Data Science Evaluation competition on converting airborne remote sensing into ecological information using data from the National Ecological Observatory Network (NEON).

The goal of this collection is to improve the methods used to convert remote sensing imagery into the kinds of information that biologists would collect in the field. Since remote sensing can be collected at much larger scales than field data this has the potential to rapidly advance our understanding of natural systems.

The competition focused on using remote sensing data recorded from airplanes to determine the location, size, and species of individual trees. This involved three tasks: 1) segmenting the imagery into individual trees; 2) aligning those remotely sensed trees with trees identified on the ground; and 3) identifying each tree to species.

The collection is composed of a main paper describing the goals, structure, and outcomes of the competition, plus a series of short papers or preprints by the participants describing their detailed methodological approaches and exploring their results and the implications for future research.

Also available to other groups