The aim is to develop a digital twin of an airport that uses computer vision modules to collect real-time data on aircraft, vehicles and operators during ground operations. This data will be integrated and visualized in various ways to improve situational awareness and support efficient operations, especially in extreme weather events. The system includes modules for video processing, data collection and integration, and visualization through maps and augmented reality, as well as a simulator to train operators in a virtual environment. The goal is to improve the resilience of the airport and decision-making processes. The activity is carried out in collaboration with SACBO, TXT e-tech, and Sorint.tek, partners in the research contract AVIO.

Related activities are:

• Computer Vision for Event Detection and Tracking
  
  
  
  
• Eye Tracking for HMI Optimization
  
  
  
  
• Deep Reinforcement Learning for Stand and Services Allocation
  
  
  
  

In particular, the CVMLab is directly involved in activities regarding: the development of methods for the state detection of vehicles and aircrafts through Deep Convolutional Neural Networks, the support for the development of Human-Machine Interface systems, and the application of Deep Reinforcement Learning techniques for the continuous improvement of airport safety procedures.