## 1 Introduction  
Nepal, a seismically active [1] and economically developing country [2], lies between India and China. In 2015, Nepal experienced a massive earthquake and many aftershocks destroying many infrastructures and lives [3; 4; 5]. In the past decade, Nepal has seen rapid population growth and unplanned urbanization leading to many infrastructure developments from rural excavated roads to national highway projects [4; 6]. Many forest areas were converted to cultivation lands, and existing cultivation lands were converted to built-up areas. These alterations are causing undulations in river courses and patterns, thus further affecting the water quantity and quality of rivers [7; 8; 9; 10; 11]. All these natural and anthropogenic activities have altered the land cover (LC) rapidly in Nepal. Regular LC mapping and change detection is an utmost important task to maintain pace with these changes. Regular monitoring of LC change through an updated database helps us access global climatic pattern change and mitigate its issues [12]. LC changes are crucial for understanding relationships and interactions between human and natural phenomena and to promote better decision-making for sustainable development [13]. Earth observation (EO) data are one of the best resources for measuring LC dynamics [14; 15; 16]. It started with the Landsat satellite mission launch and has been continuously monitoring the earth's surface for more than four decades [17; 18; 19]. While many satellites with advanced capabilities have been launched for the same purpose, Landsat has continuously evolved, with each mission maintaining its legacy. This vast collection of data is also freely available for researchers, which is a great asset to the scientific community. Many case studies have been conducted for LC mapping and change detection [20; 21; 22; 23; 24; 25; 26; 27]. Many indices have been developed to extract a single feature, whereas recent advancements in machine learning (ML) algorithms have significantly improved classification accuracy [28; 29; 30; 31; 32]. However, working on multiple satellite imageries/scenes in edge devices is tedious, time-consuming and often requires enormous storage space. One possible approach to handle such massive EO data for large spatial and temporal extents is to utilize multiple computers, which is also an expensive investment for a few times of use. An optimum solution that could solve all the above problems would be a cloud-based public computing platform. This conserves the infrastructure, cost, computing time and maximum utilization issues at one step. Google Earth Engine (GEE) is such a cloud-based computing platform that has a vast amount of satellite data. GEE makes satellite imagery processing very convenient [33; 34; 35]. This method provides free and easy data access and analyzes the massive amount of data in a matter of seconds. Some previous works in Asia, China, Africa, and the USA have shown a promising use of GEE [34; 35; 36; 37; 38; 39; 40; 41]. Though there are many conventional methods for mapping LC and its dynamics, these processes are time-consuming and require much computational power. To overcome these limitations, one possible solution is the implementation of ML algorithms that not only provide efficient computing performance in terms of power and time but also furnish more accurate results than conventional methods. With the advancement in computing technology, many studies have been conducted by combining statistical methods with machine learning models such as artificial neural networks (ANN), logistic regression (LR), decision trees (DT) and support vector machines (SVM) and has been reported to perform better than conventional methods [42; 43; 44]. Accuracy can be furthermore enhanced using the ensemble-learning methods (ELMs) that combine several models to one predictive model that decreases variance (bagging), bias (boosting) and improves predictions (stacking) that produces better accuracy than a single ML model [45; 46].