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 …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …

An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …

This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …

In recent years, the combination of deep learning and remote sensing has been a boiling
state. However, because of the difference between remote sensing images and natural …

Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …

Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …