## 4 UAVs and Artificial Intelligence
### AI and UAVs: Breakthroughs and Crossovers  
The rapid evolution of AI and UAV technologies has led to a growing crossover between the two fields. As AI techniques, such as deep learning and machine learning, continue to progress, they are being integrated into UAV technology to expand the capabilities of these systems significantly. AI enables drones to operate with more autonomy, adaptability, and intelligence than ever before. In turn, UAVs serve as ideal testbeds for applying and validating cutting-edge AI innovations, ranging from computer vision to real-time object identification. This fusion is transforming UAVs from remotely controlled devices into intelligent aerial robotic systems, and unlocking emergent applications that were not previously feasible. Key areas where AI advances are intersecting with UAVs include computer vision, autonomous navigation and flight control, human-drone teaming, swarm coordination, mission planning, and air combat tactics. **Machine Vision:** Powerful computer vision algorithms enable drones to perceive and map their surroundings for navigation and data capture. Deep neural networks can be trained on visual data to identify objects, interpret scenes, detect obstacles, and track targets. This allows UAVs to autonomously navigate unfamiliar environments, avoid collisions, and follow dynamic objects. AI-enabled computer vision also facilitates automated inspection, surveillance, and mapping tasks by UAVs. A study by [PERSON] et al. [185] emphasized the significant role that UAVs equipped with AI-driven systems played in improving the efficiency and effectiveness of railway monitoring. The synergistic relationship between UAVs and AI in this context was particularly relevant to climate change research, as it enabled more comprehensive and cost-effective data collection and analyses. The deep learning-empowered analysis of UAV imagery data facilitates rapidly mapping new developments, roads, and buildings critical for applications, such as disaster response and urban planning. Automated feature extraction through AI promises to make mapping infrastructure faster, cheaper, and more up to date. **Autonomous Flight Control:** AI techniques, such as reinforcement learning, allow drone flight controllers to improve stability, efficiency, and safety through experience. Instead of pre-programmed control, drones can dynamically optimize flight paths and handling in response to aerodynamic effects and environments. Reinforcement learning has been applied to teach complex aerial maneuvers, smooth trajectories, wind gust compensation, and obstacle avoidance [186]. In a study [187], the researchers explored the challenging task of enabling UAVs to autonomously follow forest trails, a problem of considerable importance for various applications, including wilderness mapping and search-and-rescue missions. The authors emphasized that UAVs, especially micro-aerialvehicles (MAVs), have become a viable option for navigating forested environments due to recent technological advancements, making them a valuable tool for environmental monitoring and climate change research. UAVs equipped with advanced AI equipment, such as DNNs, can navigate complex forested environments, gather data on vegetation, monitor changes in the terrain, and contribute valuable insights into the impact of climate change on forests and ecosystems. The ability to autonomously follow trails facilitates efficient data collection and enhances the role of UAVs in environmental research. More broadly, AI pilots can coordinate complex maneuvers and contingencies without human guidance. This is critical for long-duration or long-distance flights, where continuous human operation is infeasible. **Human-Drone Teaming:** To collaborate more seamlessly with human operators, AI provides drones with natural language and gesture recognition, intention prediction, and adjustable autonomy.