A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Map** high resolution (30-m or better) cropland extent over very large areas such as
continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of …

Land cover monitoring using remotely sensed data requires robust classification methods
which allow for the accurate map** of complex land cover and land use categories …

Efforts are increasingly being made to classify the world's wetland resources, an important
ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing …

The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial
pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a …

Image classification is a complex process that may be affected by many factors. This paper
examines current practices, problems, and prospects of image classification. The emphasis …

Deep Learning, and Deep Neural Networks in particular, have established themselves as
the new norm in signal and data processing, achieving state-of-the-art performance in …

In the last three decades, the technologies and methods of remote sensing have evolved
dramatically to include a suite of sensors operating at a wide range of imaging scales with …