## 1 Introduction
Some of the LU/LC classification algorithms used by researchers are Maximum Likelihood Classification (MLC), Support Vector Machine (SVM) Classification, k-Nearest Neighbor Classification (kNN), K-Means Clustering, Mahalanobis Distance Classification (MDC), Classification and Regression Tree (CART), Logistic Regression Model (LRM), Artificial Neural Network (ANN) Classification, Random Forest Classification (RFC), Spectral Angle Mapper (SAM) Classification, Minimum Distance to Mean Classification (MDM), Parallepiped Classification (PLC), Multivariate Adaptive Regression Spline (MARS), Fuzzy C Means (FCM), and Iterative Self-Organizing Data Analysis (ISODATA) clustering. The different LU/LC class types classified are built-up areas, water bodies, forest-cover areas, wetlands, and vegetation areas. The accuracy assessment was performed by comparing the LU/LC classified map with the ground truth data. Based on the accuracy assessment, the performance of the classification method has been measured. The LU/LC change detection has been performed between the LU/LC time-series classified map [18; 19; 20; 21; 22]. The LU/LC prediction was performed by calibrating the dependent and independent variables. The LU/LC change map is considered the dependent variable, and the factors associated with the LU/LC change are considered as the independent variables. The factors associated with LU/LC change include slope, elevation, aspect, climatic variables, distance variables (distance from road, forest edge, agricultural land, water bodies, and urban areas), and census data. LU/LC prediction has been performed by using different algorithms for finding the future LU/LC changes in a particular location. Some of the algorithms used by researchers are based on the Markov Chain (MC), Cellular Automata (CA), Conventional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory Neural Network (LSTM) [23; 24; 25; 26; 27; 28; 29; 30]. In recent technologies, transformer-based models are widely used and processed in image-processing applications. The transformer-based deep-learning model is considered the state-of-the-art model in image recognition, as it focuses on the confident part of inputs to get more efficient results [31; 32]. Many researchers have worked on the transformer-based model in the field of natural language processing (NLP) [33; 34]. Researchers also performed the transformer-based models in image-recognition problems through remote sensing analysis. The Vision Transformers have been used widely for remote sensing applications. The advantage of using the Vision Transformers for remote-sensing applications provides better classification accuracy than the standard algorithms [35; 36; 37]. Explainable artificial intelligence (XAI) is a process of allowing the users to understand and trust the outputs produced by the machine-learning and deep-learning models. XAI conveys the importance of transparency (presents the significant way of reaching the goal), justification (clarifying why the results provided by the prediction model are acceptable), informativeness (providing new information to researchers), and uncertainty estimation (computing how trustworthy a prediction model is) [38; 39]. The few XAI tools for explaining the results of machine-learning and deep-learning models include LIME (Local Interpretable Model-Agnostic Explanations), DeepLIFT (Deep Learning Important Features), SHAP (Shapley Additive explanations), LRP (Layer-Wise Relevance Propagation), Saliency Maps, CIU (Contextual Importance and Utility), DALEX (Model Agnostic Language for Exploration and Explanation), Skater, Occlusion Analysis, and Integrated Gradients/SmoothGrad. The usage of XAI tools varies for every application area of machine- and deep-learning models [40; 41; 42; 43]. In the field of remote sensing, we observed that researchers had used the supervised and unsupervised machine-learning models for performing the LU/LC classification and prediction analysis.