Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Scene understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …

Map** the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …

Abstract The US Geological Survey (USGS), in partnership with several federal agencies,
has developed and released four National Land Cover Database (NLCD) products over the …

Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

Object-based image classification for land-cover map** purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …

Scientific contributions from remote sensing over the last fifty years have significantly
advanced our understanding of urban areas. Key contributions of urban remote sensing …