Friedl presentation at CIDU

In this paper we discuss results from applying a variety of different data mining approaches to global remote sensing data sets. Specifically, we describe three main problem domains and sets of analyses: (1) supervised classification of global land cover from using data from NASA’s Moderate Resolution Imaging Spectroradiometer; (2) the use of linear and non-linear cluster and dimensionality reduction methods to examine coupled climate-vegetation dynamics using a twenty year time series of data from the Advanced Very High Resolution Radiometer; and (3) the use of functional models, non-parametric clustering, and mixture models to help interpret and understand the feature space and class structure of high dimensional remote sensing data sets.

The paper will not focus on specific details of algorithms. Instead we describe key results, successes, and lessons learned from ten years of research focusing on the use of data mining and machine learning methods for remote sensing and Earth science problems.