With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

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 …

Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

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 …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …