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Classification of land use/land cover using artificial intelligence (ANN-RF)

Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of mach...

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Autores principales: Alshari, Eman A., Abdulkareem, Mohammed B., Gawali, Bharti W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853425/
https://www.ncbi.nlm.nih.gov/pubmed/36686849
http://dx.doi.org/10.3389/frai.2022.964279
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author Alshari, Eman A.
Abdulkareem, Mohammed B.
Gawali, Bharti W.
author_facet Alshari, Eman A.
Abdulkareem, Mohammed B.
Gawali, Bharti W.
author_sort Alshari, Eman A.
collection PubMed
description Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).
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spelling pubmed-98534252023-01-21 Classification of land use/land cover using artificial intelligence (ANN-RF) Alshari, Eman A. Abdulkareem, Mohammed B. Gawali, Bharti W. Front Artif Intell Artificial Intelligence Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF). Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9853425/ /pubmed/36686849 http://dx.doi.org/10.3389/frai.2022.964279 Text en Copyright © 2023 Alshari, Abdulkareem and Gawali. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Alshari, Eman A.
Abdulkareem, Mohammed B.
Gawali, Bharti W.
Classification of land use/land cover using artificial intelligence (ANN-RF)
title Classification of land use/land cover using artificial intelligence (ANN-RF)
title_full Classification of land use/land cover using artificial intelligence (ANN-RF)
title_fullStr Classification of land use/land cover using artificial intelligence (ANN-RF)
title_full_unstemmed Classification of land use/land cover using artificial intelligence (ANN-RF)
title_short Classification of land use/land cover using artificial intelligence (ANN-RF)
title_sort classification of land use/land cover using artificial intelligence (ann-rf)
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853425/
https://www.ncbi.nlm.nih.gov/pubmed/36686849
http://dx.doi.org/10.3389/frai.2022.964279
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