In EDS ansehen

مقایسه و ارزیابی الگوریتم‌های مختلف یادگیری ماشین در طبقه‌بندی نقشه کاربری / پوشش اراضی با استفاده از تصاویر ماهواره‌ای ( مطالعه موردی: جنوب دریاچه ارومیه).

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Titel: مقایسه و ارزیابی الگوریتم‌های مختلف یادگیری ماشین در طبقه‌بندی نقشه کاربری / پوشش اراضی با استفاده از تصاویر ماهواره‌ای ( مطالعه موردی: جنوب دریاچه ارومیه). (Persian)
Alternate Title: Comparison and Evaluation of Different Machine Learning Algorithms in Land Use/Cover Classification Using Satellite Data (Case Study: South of Lake Urmia). (English)
Autoren: ناصر احمدی ثانی, سهراب مرادی
Quelle: Remote Sensing & GIS Applications in Environmental Sciences; 2025, Vol. 5 Issue 16, Preceding p1-18, 21p
Schlagwörter: RANDOM forest algorithms, LAND use mapping, SUPPORT vector machines, SALT lakes, SUSTAINABILITY, REGRESSION trees, GEOSPATIAL data, MACHINE learning
Geografische Kategorien: LAKE Urmia (Iran)
Abstract: Objective: Land use/cover has great importance for planning at different spatial scales in order to environmental sustainability. Land use/cover changes affects ecosystem services and products, socio-economic issues, climate change, natural resource and biodiversity. This study aimed to evaluate and compare different machine learning algorithms including classification and regression tree (CART), random forest (RF) and support vector machine (SVM) for land use/cover mapping in the south of Lake Urmia. Methods: Sentinel-2A satellite data from 2023 were used within Google Earth Engine platform. Classification was performed using sample points with 70% for training and 30% for validation. The accuracy assessment was evaluated using the overall accuracy and kappa coefficient. Results: Based on the land use / cover map, seven category were identified: water bodies, saline and rocky lands, irrigated farming, dry farming, built up areas, orchards, and ranges. The RF algorithm showed the highest overall accuracy (89%) while CART and SVM follow RF with 83% and 80%. Conclusions: This study proved that RF is the best algorithm for optimal land use/cover classification, particularly in the study area. It also emphasizes the need to conduct similar studies with more advanced algorithms along with secondary data, especially in the Lake Urmia watershed, in order to achieve sustainable development. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing & GIS Applications in Environmental Sciences is the property of University of Tabriz and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
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  Data: مقایسه و ارزیابی الگوریتم‌های مختلف یادگیری ماشین در طبقه‌بندی نقشه کاربری / پوشش اراضی با استفاده از تصاویر ماهواره‌ای ( مطالعه موردی: جنوب دریاچه ارومیه). (Persian)
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  Data: Comparison and Evaluation of Different Machine Learning Algorithms in Land Use/Cover Classification Using Satellite Data (Case Study: South of Lake Urmia). (English)
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  Data: Remote Sensing & GIS Applications in Environmental Sciences; 2025, Vol. 5 Issue 16, Preceding p1-18, 21p
– Name: Subject
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22LAND+use+mapping%22">LAND use mapping</searchLink><br /><searchLink fieldCode="DE" term="%22SUPPORT+vector+machines%22">SUPPORT vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22SALT+lakes%22">SALT lakes</searchLink><br /><searchLink fieldCode="DE" term="%22SUSTAINABILITY%22">SUSTAINABILITY</searchLink><br /><searchLink fieldCode="DE" term="%22REGRESSION+trees%22">REGRESSION trees</searchLink><br /><searchLink fieldCode="DE" term="%22GEOSPATIAL+data%22">GEOSPATIAL data</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink>
– Name: SubjectGeographic
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22LAKE+Urmia+%28Iran%29%22">LAKE Urmia (Iran)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: Land use/cover has great importance for planning at different spatial scales in order to environmental sustainability. Land use/cover changes affects ecosystem services and products, socio-economic issues, climate change, natural resource and biodiversity. This study aimed to evaluate and compare different machine learning algorithms including classification and regression tree (CART), random forest (RF) and support vector machine (SVM) for land use/cover mapping in the south of Lake Urmia. Methods: Sentinel-2A satellite data from 2023 were used within Google Earth Engine platform. Classification was performed using sample points with 70% for training and 30% for validation. The accuracy assessment was evaluated using the overall accuracy and kappa coefficient. Results: Based on the land use / cover map, seven category were identified: water bodies, saline and rocky lands, irrigated farming, dry farming, built up areas, orchards, and ranges. The RF algorithm showed the highest overall accuracy (89%) while CART and SVM follow RF with 83% and 80%. Conclusions: This study proved that RF is the best algorithm for optimal land use/cover classification, particularly in the study area. It also emphasizes the need to conduct similar studies with more advanced algorithms along with secondary data, especially in the Lake Urmia watershed, in order to achieve sustainable development. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing & GIS Applications in Environmental Sciences is the property of University of Tabriz and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.22034/rsgi.2025.65022.1116
    Languages:
      – Code: per
        Text: Persian
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 1
    Subjects:
      – SubjectFull: LAKE Urmia (Iran)
        Type: general
      – SubjectFull: RANDOM forest algorithms
        Type: general
      – SubjectFull: LAND use mapping
        Type: general
      – SubjectFull: SUPPORT vector machines
        Type: general
      – SubjectFull: SALT lakes
        Type: general
      – SubjectFull: SUSTAINABILITY
        Type: general
      – SubjectFull: REGRESSION trees
        Type: general
      – SubjectFull: GEOSPATIAL data
        Type: general
      – SubjectFull: MACHINE learning
        Type: general
    Titles:
      – TitleFull: مقایسه و ارزیابی الگوریتم‌های مختلف یادگیری ماشین در طبقه‌بندی نقشه کاربری / پوشش اراضی با استفاده از تصاویر ماهواره‌ای ( مطالعه موردی: جنوب دریاچه ارومیه).
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          Name:
            NameFull: ناصر احمدی ثانی
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            NameFull: سهراب مرادی
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            – D: 01
              M: 10
              Text: 2025
              Type: published
              Y: 2025
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              Value: 5
            – Type: issue
              Value: 16
          Titles:
            – TitleFull: Remote Sensing & GIS Applications in Environmental Sciences
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