Geo-Eye

Abstract

Land Use and Land Cover (LULC) classification is crucial for understanding and managing environmental resources. This study introduces an innovative methodology that leverages Sentinel satellite data alongside two robust machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), on the Google Earth Engine (GEE) platform. Renowned for its high-resolution multispectral imagery, Sentinel data offer rich information for classification. GEE provides access to extensive geospatial datasets and computational resources, enabling effective analysis. RF and SVM are known for their ability to handle complex datasets, optimizing classification accuracy. The study outlines a systematic workflow for preprocessing Sentinel imagery, followed by the implementation of RF and SVM algorithms, with a focus on accurately classifying vegetation, built-up areas, barren land, and water bodies. Evaluation metrics, including overall accuracy and kappa coefficient, demonstrate the efficacy of the proposed methodology. This compelling study highlights the utility of RF and SVM within GEE for precise LULC mapping, emphasizing their pivotal role in supporting informed decision-making for environmental planning and conservation initiatives.

Keywords: Google Earth Engine, Support Vector machine, Random Forest, Sentinel satellite data, Land Use and Land Cover