An automated workflow based on UAV imagery and Deep Learning methods for monitoring excavation area work

The rapid advancement of Artificial Intelligence (AI) is transforming the construction sector, particularly in site monitoring and safety management. Real-time monitoring enables the automatic detection of work progress issues, anomalies, and hazardous situations. However, no existing Deep Learning (DL)-based system is specifically designed to utilize Unmanned Aerial Vehicles (UAVs) for excavation area monitoring. This study presents an automated workflow that integrates UAV imagery with DL architectures, featuring a 1D Convolutional Neural Network (1D-CNN) for classifying excavation work phases and a VGG16 network for detecting safety fences. These technologies are incorporated into a Decision Support System (DSS), which automates report generation and enhances decision-making by providing structured, data-driven insights. The system was validated in a real-world case study involving an oil and gas construction company, demonstrating its ability to streamline site management tasks and improve safety oversight. Compared to traditional monitoring methods, our approach leverages UAV technology and DL methodologies to provide higher accuracy, efficiency, and scalability in excavation site monitoring. This contribution supports the digital transformation of construction management, offering a practical and innovative solution for real-time progress tracking and compliance verification.