## 1 Introduction  
The Land Use/Land Cover (LU/LC) prediction is one of the most significant applications of remote sensing and GIS technology. The main causes of LU/LC changes are agricultural/crop damage, wetland change, deforestation, urban expansion, and vegetation loss. Several researchers working in this application area for many years had different findings for their study areas around the world. The importance of this LU/LC prediction research is to provide information about the landscape changes of the specific study area to the government officials, forest department, urban planners, and social workers for the protection of the LU/LC environment [1; 2; 3]. Remote sensing technology provides information about the satellite data and helps in performing the LU/LC prediction research effectively. Researchers have used different remote sensing satellite systems for acquiring the data, and some of the satellite system databases are Advanced Land Imager (ALI), Hyperion data, Linear Imaging Self-Scanning Sensor III (LISS-III), Linear Imaging Self-Scanning Sensor IV (LISS-IV), Landsat Series, Sentinel-2A and -2B, Moderate Resolution Imaging Spectror-radiometer (MODIS), Rapid Eye Earth Imaging System (REIS), and ASTER Global DEM (Digital Elevation Model). Other data acquisition for performing the LU/LC prediction research can be made through aerial photographs, Google Earth images, government, and field or ground survey data. The advantage of the satellite and airborne data has been used in many applications areas such as oceanography, landscape monitoring, weather forecasting, biodiversity conservation, forestry, cartography, surveillance, and warfare [4; 5; 6; 7; 8; 9; 10]. The different band in the multispectral data has been widely used in monitoring the LU/LC changes around the world. The visible (red-blue-green), near infrared (NIR), short-waveinfrared (SWIR), and TIRS (thermal infrared sensor) bands were used for calculating the most important LU/LC indices, such as the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Difference Built-Up Index (NBBI), and Normalized Difference Salinity Index (NDSI) [11; 12]. The primary processing for correcting the noise and cloud effects in the satellite and airborne data has been achieved through preprocessing. The multispectral satellite data have been used for performing effective research on LU/LC analysis. The noise, atmospheric, geometric, topographic, and radiometric errors in the raw multispectral satellite data are corrected by using the primary process of image preprocessing. Different methods have been used for correcting the satellite image errors, and some of them are Image Registration, Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), Discrete Wavelet Transform (DWT), Resampling, Quick Atmospheric Correction (QUAC) module, Minimum Noise Fraction (MNF), Dark Object Subtraction (DOS) module, Orthorectification, Rescaling, Principal Component Analysis (PCA), F-mask method, FLAASH (Fast Line-of-Sight Atmospheric Analysis of Hypercubes) module, ASCII Coordinate Conversion, Apparent Reflectance Model (ARM), Georeferencing, Image De-striping, and Lookup Table (LUT) Stretch and Point Spread Convolution methods [13; 14; 15; 16; 17]. LU/LC classification has been performed by using different classification algorithms for finding the LU/LC types of a particular location.