A data science challenge for converting airborne remote sensing data into ecological information

A data science challenge for converting airborne remote sensing data into ecological information

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to...

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Bibliographic Details
Main Authors: Sergio Marconi, Sarah J. Graves, Dihong Gong, Morteza Shahriari Nia, Marion Le Bras, Bonnie J. Dorr, Peter Fontana, Justin Gearhart, Craig Greenberg, Dave J. Harris, Sugumar Arvind Kumar, Agarwal Nishant, Joshi Prarabdh, Sundeep U. Rege, Stephanie Ann Bohlman, Ethan P. White, Daisy Zhe Wang
Format: Article
Language:English
Published: PeerJ Inc. 2019-02-01
Series:PeerJ
Subjects:
Airborne remote sensing
Species classification
Remote sensing
Data alignment
National Ecological Observatory Network
Data science competition
Online Access:https://peerj.com/articles/5843.pdf

Internet

https://peerj.com/articles/5843.pdf

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