## 1 Introduction  
Remote sensing sensors record reflected or absorbed electromagnetic (EM) spectra of various wavelengths to identify objects according to their typical response due to varying chemical and physical properties. Each rock type may exhibit distinctive spectral absorption/reflection in the relevant electromagnetic spectrum, according to their mineral compositions [1]. Different minerals have discriminative responses in near-infrared (NIR), short-wave infrared (SWIR), and thermal infrared (TIR) spectral wavelengths [1, 2, 3]. Mapping lithological units may provide information about the spatial distribution of various rock units and their structural occurrences to interpret and map potential zones of mineralization [4, 5, 6, 7, 8, 9, 10]. Hence, researchers utilize these spectral bands to explore and identify different minerals/lithologies through different techniques applied to remote sensing (RS) data integrated by field surveys and lab geochemical/spectral data [3]. Traditional remote sensing, e.g., band ratios, MNF, decorrelation stretching, and principal component analysis (PCA) [5; 6; 9; 11; 12; 13; 14; 15; 16; 17], has been widely applied to mainly ASTER [5; 9; 10; 12; 16; 17; 18; 19; 20; 21], Landsat 8 OLI [5; 10; 14], and Sentinel-2 [11; 12; 13], or their fused combination, to map different lithologies [10; 22]. Researchers have reported better results for the use of Sentinel-2 data after the application of these traditional techniques [11]. However, creating geological maps through remote sensing data is still challenging, with complexities involving sub-pixel-level nonlinear mixing of minerals response [23]. In addition, these techniques are time-consuming, requiring considerable manual interpretation and expert knowledge [24]. The mixed albedo from different lithologies, instrumental limitations, and environmental factors invite robust data mining machine learning (ML) algorithms and deep learning (DL) algorithms to reveal detailed geological information [4; 8; 25; 26; 27; 28; 29; 30]. Machine learning algorithms have been broadly classified into (1) supervised, (2) unsupervised, (3) self-supervised, and (4) reinforcement learning algorithms [31]. Supervised algorithms, e.g., naive Bayes, random forests, SVM, ANN [32], and DL algorithms [31], such as convolution neural network (CNN) and recurrent neural network (RNN), are used to predict the outputs matching given targets. Unsupervised learning, e.g., k-nearest neighborhood, fuzzy-c-means, PCA, and ICA, learn by finding interesting features in data through reconstruction, transformation, or classification without considering matching of input with targets [32]. The self-supervised class of algorithms match the targets with output data; however, the targets are self-generated from the input data instead of given targets [31]. Reinforcement learning generally involves information retrieval from the changing environment and learning to take the best action, reinforced by a reward maximization mechanism [33]. ML algorithms are a widely used alternative to other techniques for robust and automated classification [3; 4; 6; 30; 34]. However, the ML algorithms' accuracy depends on data labels that can be defective due to lithological complexity or lower spatial resolution [35]. Therefore, unsupervised techniques such as clustering, PCA, and decorrelation stretching (DS) are used in the preprocessing stage to improve the reliability of data annotation before applying the ML algorithm [6; 28]. The remote sensing dataset's spatial/spectral resolution and available bands are crucial for selecting multispectral datasets to minimize data noise [24; 35]. The freely available 10 m spatial resolution blue (B2), green (B3), red (B4), and NIR (B8), and 20 m SWIR1 (B11) and SWIR2 (B12) bands of Sentinel-2 MSI dataset in Google Earth Engine (GEE) facilitate economic means of lithological mapping [34; 36]. GEE contains observations from aerial imaging systems and multispectral satellite data related to various remote sensing (RS) domains [6; 34; 37].