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.

Predicting future connections among Unmanned Aerial Vehicles (UAVs) is one of the fundamental tasks in UAV Ad Hoc Networks (UANETs). In adversarial environments where UAV operational information is unavailable, future link prediction must rely solely on the observed historical topo logical data. However, the highly dynamic and sparse nature of UANET topologies poses substantial challenges in capturing structural and temporal link formation patterns. Most existing link prediction methods focus only on single-scale structural features while neglecting the effects of network sparsity, thus limiting their performance when applied to UANETs. In this paper, we propose MUST, a Multi-scale Structural-Temporal link prediction model for UANETs. In our model, multi-scale structural representations are learned using a weighted graph attention network combined with multi-scale pooling, capturing features at the levels of individual UAVs, UAV communities, and the entire network, which are then fused via concatenation. Then, a stacked long short-term memory network is employed to learn the temporal dynamics of these multi-scale structural features. To address the impact of network sparsity, we develop a tailored loss function that emphasizes the contribution of existing links during training. We validate the performance of MUST using several UANET datasets generated through simulations. Extensive experimental results demonstrate that MUST achieves state-of-the-art link prediction performance in highly dynamic and sparse UANETs.

Many state-of-the-art stereo matching algorithms based on deep learning have been proposed in recent years, which usually construct a cost volume and adopt cost filtering by a series of 3D convolutions. In essence, the possibility of all the disparities is exhaustively represented in the cost volume, and the estimated disparity holds the maximal possibility. The cost filtering could learn contextual information and reduce mismatches in ill-posed regions. However, this kind of methods has two main disadvantages: 1) cost filtering is very time-consuming, and it is thus difficult to simultaneously satisfy the requirements for both speed and accuracy; 2) thickness of the cost volume determines the disparity range which can be estimated, and the pre-defined disparity range may not meet the demand of practical application. This paper proposes a novel real-time stereo matching method called RLStereo, which is based on reinforcement learning and abandons the cost volume or the routine of exhaustive search. The trained RLStereo makes only a few actions iteratively to search the value of the disparity for each pair of stereo images. Experimental results show the effectiveness of the proposed method, which achieves comparable performances to state-of-the-art algorithms with real-time speed on the public large-scale testset, i.e., Scene Flow.

Some stereo matching algorithms based on deep learning have been proposed and achieved state-of-the-art performances since some public large-scale datasets were put online. However, the disparity in smooth regions and detailed regions is still difficult to accurately estimate simultaneously. This paper proposes a novel stereo matching method called WaveletStereo, which learns the wavelet coefficients of the disparity rather than the disparity itself. The WaveletStereo consists of several sub-modules, where the low-frequency sub-module generates the low-frequency wavelet coefficients, which aims at learning global context information and well handling the low-frequency regions such as textureless surfaces, and the others focus on the details. In addition, a densely connected atrous spatial pyramid block is introduced for better learning the multi-scale image features. Experimental results show the effectiveness of the proposed method, which achieves state-of-the-art performance on the large-scale test dataset Scene Flow.