## 2 Literature Review
### Machine Guidance in Earthworks Operations  
Over the past several decades, widely ranging strategies have been developed for the control of heavy construction equipment, from conventional manual methods to highly advanced automated systems. Table 1 presents a comparative overview of four key approaches to excavation control--conventional, machine guidance, remote control, and fully automated-- and summarizes their operational approaches, quality control methods, and key enabling technologies. Conventional methods rely on direct human operation and traditional surveying techniques, whereas GNSS and IMU-based machine guidance systems have recently experienced significant adoption due to their ability to provide real-time feedback and enhanced precision. In addition, remote control technologies are increasingly being used in challenging environments, such as sites that are remote or hazardous, and fully automated systems leveraging robotics and artificial intelligence are beginning to emerge. Numerous studies have investigated the potential benefits of automation and machine guidance systems for construction equipment, particularly in the context of earthwork operations. Relevant research conducted in the United States indicates that contractors and subcontractors utilizing automated machine guidance technologies anticipate a range of benefits [4]. These anticipated advantages are multifaceted and encompass a variety of operational improvements. Specifically, users expect the implementation of these technologies to contribute to a reduction in the number of workers, which is a crucial factor in enhancing overall workplace safety. Furthermore, it is anticipated that decreases in the time taken to complete activities will lead to more efficient execution of projects. In addition to these improvements in speed and safety, the adoption of automated machine guidance is projected to result in improved quality control and quality assurance, ensuring a higher standard of construction outputs. Ultimately, these combined factors are expected to enhance overall operational efficiency, streamlining workflows and optimizing resource allocation. Research specifically regarding excavator automation has indicated that integrating intelligent systems into existing mining excavators can enhance safety, productivity, and energy efficiency [13]. Intelligent excavators typically incorporate an in-cab monitor that provides real-time status updates and guidance to operators, alongside a remote monitoring portal. Data are collected from a suite of sensors, including ruggedized cameras overlooking the bucket, high-resolution surveillance cameras, radar, arm-geometry sensors, hydraulic pressure monitors, and sensors for measuring electric motor power. Although the concept of the intelligent excavator has been explored by researchers in various countries,including Korea [2; 14; 15; 16] and Japan [17], its practical implications and commercial viability remain unclear, and no fully commercial intelligent excavators are currently available. The Built Robotics company introduced a prototype autonomous excavator in 2019, although their subsequent focus has shifted towards specific applications like automated solar piling and trenching [18]. This strategic pivot suggests an acknowledgement of the inherent complexities and unstructured nature of general earthwork operations, which pose significant challenges for full autonomy. Further research in the field has explored the use of artificial neural networks trained on human-labeled video frames to perform object localization within the excavator's operational environment [3; 13]. This technology has been applied to monitor the wear and breakage of ground engaging tools, detect foreign objects within the bucket, analyze the size distribution of excavated material, and automatically provide guidance to the operator via the in-cab monitor.