Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

Flood management and media production planning are both tasks that require timely and sound decision making, as well as effective collaboration between professionals in a team split between remote headquarter operators and in situ actors. This paper presents an extended reality (XR) platform that utilizes interactive and immersive technologies and integrates artificial intelligence (AI) algorithms to support the professionals and the public involved in such incidents and events. The developed XR tools address various specialized end-user needs of different target groups and are fueled by modules that intelligently collect, analyze, and link data from heterogeneous sources while considering user-generated content. This platform was tested in a flood-prone area and in a documentary planning scenario, where it was used to create immersive and interactive experiences. The findings demonstrate that it increases situation awareness and improves the overall performance of the professionals involved. The proposed XR system represents an innovative technological approach for tackling the challenges of flood management and media production, one that also has the potential to be applied in other fields.