Since the 1990s, remote sensing images have been used for land cover classification combined with Machine
Learning algorithms. The traditional land surveying method only works well in places that are hard to get to, like
high mountain regions, arid and semi-arid land, and densely forested areas. As the satellites and airborne sensors
pass over a specific point of land surface periodically, it is possible to assess the change in land cover over a long
time. With the advent of ML methods, automated land cover classification has been at the center of research
for the last few decades. From 2015 forward, a technical shift has been noticed with the emergence of several
branches of Neural Networks (NN) and Deep Learning (DL). This paper examines current practices, problems,
and trends in satellite image processing. This formal review focused on the summarization of major classification
approaches from 1995. Two dominant research trends have been noticed in automated land cover classification,
e.g., per pixel and subpixel analysis. Classical machine learning algorithms and deep learning methods are mainly
used for per-pixel analysis, whereas Fuzzy algorithms are used for sub-pixel analysis. The current article includes
the research gap in automated land cover classification to provide comprehensive guidance for subsequent research
This work is licensed under a Creative Commons Attribution 4.0 International License.
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