D.K. McIver

TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.

TL;DR: A method for incorporating prior probabilities in remote-sensing-based land cover classification using a supervised decision-tree classification algorithm that allows robust probabilities of class membership to be estimated from nonparametric supervised classification algorithms using a technique known as boosting.

TL;DR: The authors present a method to estimate pixel-scale land cover classification confidence using nonparametric machine learning methods based on recent theoretical developments from the domains of statistics and machine learning that explain the machine learning technique known as "boosting" as being equivalent to additive logistic regression.

TL;DR: In this article, the authors describe the compilation and assessment of a new map of North American land cover produced through the application of advanced pattern recognition techniques to multitemporal satellite data.

TL;DR: In this paper, the authors focus on optical remote sensing technologies, including airborne radar, video imaging systems, and satellite instruments with high spatial and spectral resolution, which provide detailed information regarding topography and vegetation structure in three dimensions.