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- W4313372243 abstract "Land use land cover (LULC) is a significant component of remote sensing since it is employed in a variety of analyses, from change detection to geographic modeling. As a result, creating an accurate LULC map is critical. Three different pixel-based classification algorithms [i.e., maximum likelihood (ML), neural networks (NN) and random forests (RF)] were utilized to examine their relative performance in generating remotely sensed LULC maps in the current study. The research was carried out using high-resolution satellite images. The classification results are evaluated using accuracy measures derived from the confusion matrix. The findings suggest that it is difficult to achieve higher accuracy in classifying large urban areas using a 5 m resolution satellite dataset. The comparative results indicate that random forests have outperformed ML and NN in classifying the urban land cover using a high-resolution image. The user and producer accuracies of LULC are found to show no particular trend with any classification algorithm." @default.
- W4313372243 created "2023-01-06" @default.
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- W4313372243 date "2022-01-01" @default.
- W4313372243 modified "2023-09-26" @default.
- W4313372243 title "Comparison of Maximum Likelihood, Neural Networks, and Random Forests Algorithms in Classifying Urban Landscape" @default.
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- W4313372243 doi "https://doi.org/10.1007/978-3-031-14096-9_2" @default.
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