## 1 Introduction  
Depending on the usefulness of Earth's observational data, various fields such as regional and urban planning, impact assessment, environmental vulnerability, monitoring of hazards and natural disasters, and the prediction of salinity and soil erosion, among others, require an understanding of LULC change [1]. Potential approaches to comprehend and managing landscape data include quantitative LULC dynamics prediction and assessment [2]. It was discovered that mapping LULC change is a crucial component of a wide range of applications and activities, including global warming mitigation and land use planning. Evaluation of LULC change is therefore crucial for a number of reasons related to human wellbeing, including unchecked and rapid population expansion as well as industrial and economic development, especially in emerging countries with amplified LULC shifts [3]. Such changes have effects on the environment and human society in several ways, such as groundwater depletion, rising drought and flood vulnerability, landslide hazards, loss of ecosystem services, soil erosion, environmental degradation, and many more [4]. Many approaches were advanced to map LULC dynamics and patterns from satellite observations which include conventional terrestrial mapping along with satellite-related mapping [5]. Terrestrial mapping, otherwise called a field survey, is a straight way of mapping where the map is generated at numerous scales integrating data with distinct accuracy levels, though it is a manpower-related, money-and-time-consuming way to map big regions [6]. In contrast, the aerial-and-satellite photograph-related maps of LULC are multi-temporal, inexpensive, time-saving, and spatially extensive [7]. Remote Sensing (RS) presents the chance for fast data acquisition on LULC at an affordable cost than other techniques, such as ground surveys. The satellite images have the merits of multitemporal accessibility along with extensive spatial coverage for LULC mapping [8]. Previously, more advanced techniques, such as decision trees, Artificial Neural Networks (ANNs), Random Forest (RF), and other methods, have grabbed special attention in RS-oriented applications, such as LULC classification. The classification of remote-sensing images (RSI) is generally attained through Machine Learning (ML) techniques and Deep Learning (DL) will be the correct pattern. DL has recently developed as a discipline utilized in Earth sciences and RS [9]. Many geoscience topics deal with precipitation nowcasting, extreme weather patterns, carbon fluxes prediction, and climate change projections found in the literature. Similarly, there is a wide range of RS topics, such as registration and image fusion, image segmentation, and (drought) forecasting change identification, that include DL techniques [10]. The [11] explored deep semantic data of high-spatial and temporal resolution time series power datasets for exploring its association with socio-economic features and framed a Neural Network (NN) (TR-CNNs) is integrate fuse time-series electricity datasets and RSI for detecting urban land-use types. In [12], new multi-scale DL methods, ResASPP-Unet and ASPP-Unet, were devised for LULC. In [13], the authors compared the classifier performance of four non-parametric methods; they are Xgboost, Support Vector Machine (SVM), DL, and RF. The study area selected is a complicated mixed-use land in south-central Sweden, having eight LCLU classes. The satellite images utilized for classifying are multitemporal scenes from Sentinel-2 that cover autumn, spring, winter, and summer conditions. In [14], the authors initially devised a Siamese global learning (Siam-GL) structure, which is a new semantic change detection structure for HSR- RSI. In [15], OpenStreetMap (OSM) is utilized for delineating parcels that are identified as basic mapping units. The semantic segment of street view imageries is integrated to boost the multi-dimensional descriptions of urban parcels, plus Luojia-1 nighttime light data, Sentinel-2A, and point of interest (POI).