Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit...

Full description

Bibliographic Details
Main Authors: Arthur Elmes, Hamed Alemohammad, Ryan Avery, Kelly Caylor, J. Ronald Eastman, Lewis Fishgold, Mark A. Friedl, Meha Jain, Divyani Kohli, Juan Carlos Laso Bayas, Dalton Lunga, Jessica L. McCarty, Robert Gilmore Pontius, Andrew B. Reinmann, John Rogan, Lei Song, Hristiana Stoynova, Su Ye, Zhuang-Fang Yi, Lyndon Estes
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Remote Sensing
Subjects:
training data
machine learning
map accuracy
error propagation
Online Access:https://www.mdpi.com/2072-4292/12/6/1034

Internet

https://www.mdpi.com/2072-4292/12/6/1034

Similar Items