Improved Inference and Prediction for Imbalanced Binary Big Data Using Case-Control Sampling: A Case Study on Deforestation in the Amazon Region

Improved Inference and Prediction for Imbalanced Binary Big Data Using Case-Control Sampling: A Case Study on Deforestation in the Amazon Region

It is computationally challenging to fit models to big data. For example, satellite imagery data often contain billions to trillions of pixels and it is not possible to use a pixel-level analysis to identify drivers of land-use change and create predictions using all the data. A common strategy to r...

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Bibliographic Details
Main Authors: Denis Valle, Jacy Hyde, Matthew Marsik, Stephen Perz
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
satellite imagery
deforestation
inference
prediction
Amazon
pixel sampling
Online Access:https://www.mdpi.com/2072-4292/12/8/1268

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