Performance Assessment of Maximum Likelihood, Random Forest and Support Vector Machines Classifier for Urban Land Use Classification: A Case Study of Dhaka Metropolitan City, Bangladesh

Performance Assessment of Maximum Likelihood, Random Forest and Support Vector Machines Classifier for Urban Land Use Classification: A Case Study of Dhaka Metropolitan City, Bangladesh

Segmentation of remotely sensed satellite images is obligatory for multifarious earth observation studies, including land use and land cover (LULC) analysis. It is also inherent in environment, ecosystem, and urban development in analytical perspectives and complex inputs for modeling urban planning...

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Bibliographic Details
Main Authors: Ha-mim Ebne Alam, Md. Nizam Uddin, Kazi Tawkir Ahmed, Md. Jahidul Hasan, Md. Yeasir Arafat, Md. Enamul Hoque
Format: Article
Language:English
Published: Universitas Gadjah Mada 2025-07-01
Series:Journal of Geospatial Information Science and Engineering
Subjects:
land cover classification, urban gis, remote sensing, land cover classification accuracy
Online Access:https://jurnal.ugm.ac.id/jgise/article/view/73283
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https://jurnal.ugm.ac.id/jgise/article/view/73283

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