Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms
Land use and land cover (LULC) mapping provides crucial information for sustainable development, urban planning, disaster risk assessment, and mitigation. Various approaches are used for LULC classification in remote sensing, but machine learning has recently gained significant popularity. This pape...
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| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/863/2025/isprs-annals-X-G-2025-863-2025.pdf |
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| author | M. Sultan N. Saleous S. Issa B. Dahy M. Sami |
| author_facet | M. Sultan N. Saleous S. Issa B. Dahy M. Sami |
| author_sort | M. Sultan |
| collection | DOAJ |
| description | Land use and land cover (LULC) mapping provides crucial information for sustainable development, urban planning, disaster risk assessment, and mitigation. Various approaches are used for LULC classification in remote sensing, but machine learning has recently gained significant popularity. This paper investigates the application of machine learning algorithms for LULC mapping in Al Ain city, UAE. The study utilizes the Gradient Tree Boosting (GTB), Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) classifiers within the Google Earth Engine (GEE) platform. The objective is to evaluate and compare the performance of these algorithms using Sentinel-2 imagery from 2024 while also assessing GEE’s suitability for handling both the dataset and algorithms. Various parameters influence algorithm performance. Algorithm performance is evaluated based on overall accuracy and kappa coefficient metrics along with user and producer accuracy. The results indicate that RF and GTB achieved the highest overall accuracy, with GTB's Kappa coefficient slightly lower than RF’s, followed by SVM. CART demonstrated a comparatively lower overall accuracy and Kappa coefficient than the other classifiers. These findings provide insights into the suitability of these algorithms and highlight GEE’s limitations -particularly its memory constraints- for LULC mapping in arid environments like Al Ain. This research contributes to the development of LULC mapping methodologies and their applicability in a sustainable development context. |
| format | Article |
| id | doaj-art-b5d5dc4c15db4e50aed9972afd1469d4 |
| institution | OA Journals |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-b5d5dc4c15db4e50aed9972afd1469d42025-08-20T02:37:23ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202586386910.5194/isprs-annals-X-G-2025-863-2025Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning AlgorithmsM. Sultan0N. Saleous1S. Issa2B. Dahy3M. Sami4Department of Geosciences, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, UAEDepartment of Geography and Urban Sustainability, College of Humanities and Social Sciences, United Arab Emirates University, Al Ain, Abu Dhabi, UAEDepartment of Geosciences, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, UAEDivision of Engineering, New York University (NYU Abu Dhabi), PO Box 129188, Abu Dhabi, UAEDepartment of Geosciences, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, UAELand use and land cover (LULC) mapping provides crucial information for sustainable development, urban planning, disaster risk assessment, and mitigation. Various approaches are used for LULC classification in remote sensing, but machine learning has recently gained significant popularity. This paper investigates the application of machine learning algorithms for LULC mapping in Al Ain city, UAE. The study utilizes the Gradient Tree Boosting (GTB), Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) classifiers within the Google Earth Engine (GEE) platform. The objective is to evaluate and compare the performance of these algorithms using Sentinel-2 imagery from 2024 while also assessing GEE’s suitability for handling both the dataset and algorithms. Various parameters influence algorithm performance. Algorithm performance is evaluated based on overall accuracy and kappa coefficient metrics along with user and producer accuracy. The results indicate that RF and GTB achieved the highest overall accuracy, with GTB's Kappa coefficient slightly lower than RF’s, followed by SVM. CART demonstrated a comparatively lower overall accuracy and Kappa coefficient than the other classifiers. These findings provide insights into the suitability of these algorithms and highlight GEE’s limitations -particularly its memory constraints- for LULC mapping in arid environments like Al Ain. This research contributes to the development of LULC mapping methodologies and their applicability in a sustainable development context.https://isprs-annals.copernicus.org/articles/X-G-2025/863/2025/isprs-annals-X-G-2025-863-2025.pdf |
| spellingShingle | M. Sultan N. Saleous S. Issa B. Dahy M. Sami Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms |
| title_full | Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms |
| title_fullStr | Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms |
| title_full_unstemmed | Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms |
| title_short | Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms |
| title_sort | optimizing land use classification using google earth engine a comparative analysis of machine learning algorithms |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/863/2025/isprs-annals-X-G-2025-863-2025.pdf |
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