Land Cover Classification of Mosul Planning Using the Gray Wolf Algorithm
DOI:
https://doi.org/10.31272/jeasd.2993Keywords:
Classification, Gray wolf algorithm, Image processing, Land coverAbstract
The land cover is constantly affected by natural factors such as floods, earthquakes, and fires, as well as human activities like wars and population displacement. In this paper, a new technique is presented for classifying the land cover in the Mosul province using the Gray Wolf Algorithm (GWA). The research objective is to increase the classification accuracy rate and the efficiency of land cover classification in the Mosul planning region by using the GWA’s abilities. The research improves land cover classification performance, which advances remote-sensing wisdom applications. The findings of this study present an accurate method for detailed domain classification that can be used for various land management scenarios involving environmental planning. Swarm intelligence emerges as a strategic research tool for geographic and ecological investigations because it shows effectiveness in analyzing large-scale land surface types. The GWA was implemented using MATLAB, and five classes were identified from the image. Each class (water, soils, outcrops, slope deposits, and accumulation glaciers) had a specific color to serve as a class indicator. Good results were obtained, and the accuracy was calculated after comparing it with real data, yielding 90.56%.
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Copyright (c) 2026 Joanna Yahya, Almas Ahmed Khaleel (Author)
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