Social and Behavioral Sciences Commons^™

Accounting For Training Data Error In Machine Learning Applied To Earth Observations, 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

Geography

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 training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is …