A Comprehensive Taxonomy of UAVs: A Comparative Feature Matrix for Hardware, Capabilities, and Payloads

This survey consolidates the state of the art in vision-based unmanned aerial vehicle (UAV) systems and introduces a unifying taxonomy and feature matrix that relate platform form-factors, onboard capabilities, and payload characteristics to application domains. We systematically review and classify recent literature across agriculture, transportation and logistics, infrastructure inspection, search and rescue, environmental monitoring, emergency response, surveying/mapping, and surveillance, and analyze operational trade-offs of prominent platform classes-multi-rotor, fixed-wing, and hybrid UAVs-with respect to endurance, payload capacity, maneuverability, sensing modalities (i.e., red-green-blue (RGB) cameras and thermal sensors), and onboard computation. Algorithmic trends are synthesized, highlighting advances in lightweight deep learning (DL) techniques (notably convolutional neural network (CNN)-based perception), robust state estimation via visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM), and multisensor fusion. Recurring challenges are identified, including limited flight endurance, resilient operation in Global Positioning System (GPS)-denied or cluttered environments, scarcity of domain-specific datasets for vision tasks, and the absence of standardized benchmarks and safety frameworks. Finally, we outline research directions to promote resilient, explainable, and energy-aware UAVs and to accelerate the transition from experimental prototypes to operational deployments.