Evaluating the Performance of Classification Algorithms for Land-Cover Classification

Marcos Pastorini, Angela Gorgoglione, Rafael Rodríguez, Lorena Etcheverry, Alberto Castro

Abstract: 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.

External IDs:dblp:conf/icmla/PastoriniGREC23