Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach

Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach

ABSTRACTAccurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides critical information for planning and management of urban environments. While several studies have investigated the significance of machine learning classifiers for urban land-use mapping, the determination of...

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Bibliographic Details
Main Authors: Yashon O. Ouma, Amantle Keitsile, Boipuso Nkwae, Phillimon Odirile, Ditiro Moalafhi, Jiaguo Qi
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:European Journal of Remote Sensing
Subjects:
Urban land-use land-cover
Gradient tree boosting
Random forest
Support vector machine
Multilayer perceptron neural networks
Post-classification feature fusion
Online Access:https://www.tandfonline.com/doi/10.1080/22797254.2023.2173659

Internet

https://www.tandfonline.com/doi/10.1080/22797254.2023.2173659

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