In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification.

For resource management, urban planning, and environmental monitoring, accurate land use and land cover (LULC) categorization is essential. Nevertheless, spectral similarity across land cover categories, feature redundancy, and the shortcomings of conventional classification methods make it difficult to achieve high classification accuracy. The majority of research uses either object-based (OB) or pixel-based (PB) classification, not making use of their complementing advantages. Furthermore, feature selection is frequently done by manual analysis which does not necessarily provide accurate classification results. This paper suggests a hybrid OB-PB classification strategy in which OB classification is used for fine-tuning mapping after PB classification is used for initial segmentation. Recursive feature elimination (RFE) is an automated feature selection method that replaces traditional manual selection of textural (GLCM) and statistical (PCA) features in order to further improve classification accuracy. The study's emphasis is Rajasthan, India, which spans 219,676.75 km² and is divided into five LULC types: desert, agricultural, water, forest, and urban. Four machine learning classifiers Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Tree Boosting (GTB) were used with 249 training examples. The kappa coefficient and the confusion matrix were used to evaluate accuracy. The results show that the hybrid OB-PB technique performs better in terms of segmentation and classification precision than the independent OB and PB approaches. The classifier's eligibility for LULC mapping was confirmed by GTB's greatest accuracy among the others. For LULC classification, this paper offers a unique framework that combines automated feature selection with hybrid classification, providing a more precise, effective, and scalable method. Improved classification techniques and better decision-making for ecological surveillance and management of land are two benefits of the research.