Multi‐Task Learning for Airport Surface Surveillance: A Review

ABSTRACT The rapid growth of air transportation has surpassed the capabilities of traditional airport surveillance methods, such as visual observation and auxiliary equipment (e.g., ADS‐B, MLAT, radar), which struggle to provide all‐area, all‐weather situation awareness. Vision‐based deep learning methods, being cost‐effective and scalable, present promising alternatives but often fall short in delivering comprehensive awareness. Multi‐task learning (MTL) addresses these gaps by enabling models to simultaneously learn multiple related tasks, improving overall perception and decision‐making. Thus, this review reviews MTL systems for airport surface surveillance, categorising tasks into scene perception for intensive estimation and monitoring for non‐intensive estimation. This review identifies three key challenges: (1) efficient information sharing across tasks, (2) balancing multiple tasks, and (3) enhancing model training efficiency. The review examines these challenges through three lenses: neural network architecture, loss function optimization, and learning paradigms, proposing optimization strategies for each. As the first review to focus on MTL in airport surveillance, this review provides valuable insights into model design, task balancing, and training strategies, offering guidance for the future development of intelligent airport monitoring systems.