Evaluating the Performance of Classification Algorithms for Land-Cover Classification.
International Conference on Machine Learning and Applications(2023)
摘要
Over the past few decades, the growing population in developing countries has significantly impacted land use and land cover (LULC), resulting in a threat to natural resources. Therefore, monitoring LULC changes in critical areas for effective land-use planning and policy-making is crucial. Google Earth Engine (GEE) cloud computing is a new platform that processes geospatial data and classifies LULC over vast areas utilizing machine-learning classification algorithms. In this study, we tested several classification models using Python and GEE to evaluate their accuracy and reliability in reproducing the LULC of a watershed located in Uruguay. We aimed to address the limited availability of GEE models. Our findings indicated that the Histogram-based Gradient Boosting Classifier outperforms the other models and delivers an improved performance of 21% compared to the model implemented in GEE.
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关键词
Machine Learning,Classification Algorithms,Land-Cover Map,Google Earth Engine
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