## 1 Introduction  
The construction industry is one of the most significant contributors to the development and economic growth of a country [1]. However, the low utilization of technology and its labor-intensive nature are ongoing concerns for project managers and stakeholders [2]. With the increasing demand for proactive maintenance and operational efficiency in earthwork machinery operations, especially in the context of Industry 4.0 and 5.0, artificial intelligence (AI) has emerged as a transformative solution [3, 4, 5]. Integrating AI-based techniques such as deep learning and sensor fusion enables not just activity recognition but also intelligent fault detection and anomaly tracking [6]. A reliable activity recognition system serves as a foundational component of future AI-based health monitoring and digital twin frameworks in construction equipment operations [6; 7]. Manual observation of the construction site remains the primary source for productivity estimation and safety monitoring of the crew and construction equipment [8]. Hydraulic excavators perform various operations at earthwork construction sites due to their maneuverability and robustness. Additionally, the risks associated with the harsh and hazardous working environments of construction sites encourage the automation of excavator operations [9]. Automating the detection of excavator activities during earthmoving operations enhances the safety, monitoring, and allocation of construction resources [10]. Internet of Things (IoT) and computer vision techniques are being explored for the automatic detection, action recognition, and productivity estimation during construction. The IoT-based approach utilizes global positioning systems (GPS), radio frequency identification (RFID), and inertial measurement units (IMUs), including accelerometers and gyroscopes, to detect, track, and monitor construction productivity [11]. IoT is a widely used technology that involves electric sensors attached to construction equipment and its components to understand construction activity and estimate productivity by obtaining location coordinates and interpreting inertial values of physical motion [12; 13; 14]. However, the IMU-based single-sensor solutions are sensitive to inherent sensor constraints, highly affected in harsh environments, and limit the information acquisition capabilities of construction equipment. Technological development supported by computer vision (CV) has been known as a robust approach to improving the level of automation in the construction industry [15]. Computer vision techniques have been widely used for activity recognition and productivity estimation of construction equipment [16; 17; 18]. Several algorithms have been used to perform the activity recognition of construction equipment [19; 20; 21; 22]. A CNN network exploits spatial correlation in visual data to assist LSTM in processing data sequences and predicting excavator activities [23; 24; 25]. Additionally, the optical flow of construction machines and their components is evaluated and used during the training of various algorithms for activity recognition of construction machines [26]. Multiple algorithms have also used optical streams for the activity recognition of construction equipment [17; 23]. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This reliance on single-source data often leads to issues such as occlusions, limited viewpoints, and sensitivity to varying environmental conditions, thereby hindering continuous and accurate monitoring of excavator operations in complex construction environments. This constraint limits the model's ability and practical adoption in real-world construction scenarios. Therefore, an additional visual data source is necessary for continuous streaming of construction machine progress monitoring, improving the accuracy of the model training process for activity recognition.