Evaluating the Performance of Classification Algorithms for Land-Cover Classification

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 E...

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Bibliographic Details
Main Authors: Pastorini, Marcos, Gorgoglione, Angela, Rodriguez, Rafael, Etcheverry, Lorena, Castro, Alberto
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Earth
Google Earth Engine
Land-Cover Map
Machine Learning
Natural resources
Planning
Reliability
Sociology
Watersheds
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Summary: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.
ISSN:1946-0759
DOI:10.1109/ICMLA58977.2023.00338