Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt

Rehab Mahmoud (), Mohamed Hassanin, Haytham Al Feel and Rasha M. Badry
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Rehab Mahmoud: Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum 63514, Egypt
Mohamed Hassanin: Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum 63514, Egypt
Haytham Al Feel: Faculty of Applied College, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Rasha M. Badry: Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum 63514, Egypt

Sustainability, 2023, vol. 15, issue 12, 1-21

Abstract: Satellite images provide continuous access to observations of the Earth, making environmental monitoring more convenient for certain applications, such as tracking changes in land use and land cover (LULC). This paper is aimed to develop a prediction model for mapping LULC using multi-spectral satellite images, which were captured at a spatial resolution of 3 m by a 4-band PlanetScope satellite. The dataset used in the study includes 105 geo-referenced images categorized into 8 LULC different classes. To train this model on both raster and vector data, various machine learning strategies such as Support Vector Machines (SVMs), Decision Trees (DTs), Random Forests (RFs), Normal Bayes (NB), and Artificial Neural Networks (ANNs) were employed. A set of metrics including precision, recall, F-score, and kappa index are utilized to measure the accuracy of the model. Empirical experiments were conducted, and the results show that the ANN achieved a classification accuracy of 97.1%. To the best of our knowledge, this study represents the first attempt to monitor land changes in Egypt that were conducted on high-resolution images with 3 m of spatial resolution. This study highlights the potential of this approach for promoting sustainable land use practices and contributing to the achievement of sustainable development goals. The proposed method can also provide a reliable source for improving geographical services, such as detecting land changes.

Keywords: machine learning; remote sensing; spatial data model; LULC; mapping generation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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