Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms

Keyphrases

• Comparative Analysis 100%
• Land Use Land Cover 100%
• Machine Learning Algorithms 100%
• Google Earth Engine 100%
• Land Use Classification 100%
• Land Cover Mapping 80%
• Overall Accuracy 60%
• Kappa Coefficient 60%
• Sustainable Development 40%
• Support Vector Machine 40%
• Algorithm Performance 40%
• Random Forest 40%
• Classification and Regression Tree 40%
• Gradient Boosted Trees 40%
• Remote Sensing 20%
• Al Ain City 20%
• Urban Planning 20%
• Parameter Influence 20%
• Arid Environment 20%
• Mapping Method 20%
• Machine Learning 20%
• Decision Tree Classifier 20%
• Land Cover Classification 20%
• Sentinel-2 Images 20%
• Machine Learning Applications 20%
• Support Vector Classification 20%
• Google Earth Engine Platform 20%
• Producer's Accuracy 20%
• User's Accuracy 20%
• Disaster Risk Mitigation 20%
• Disaster Risk Assessment 20%
• Developmental Context 20%
• Memory Constraints 20%

Earth and Planetary Sciences

• Land Cover 100%
• Machine Learning 100%
• Google Earth Engine 100%
• Land Use 100%
• Land Use Classification 100%
• Comparative Analysis 100%
• Support Vector Machine 60%
• Environmental Impact Assessment 40%
• United Arab Emirates 20%
• Arid Environment 20%
• Urban Planning 20%
• Sentinel-2 20%
• Remote Sensing 20%
• Urban Development 20%

Computer Science

• Comparative Analysis 100%
• Machine Learning Algorithm 100%
• Kappa Coefficient 100%
• Support Vector Machine 66%
• Algorithm Performance 66%
• Random Decision Forest 66%
• Regression Tree 66%
• Gradient Boosting Tree 66%
• Risk Mitigation 33%
• Machine Learning 33%
• Learning System 33%