# Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

arXiv: https://arxiv.org/abs/1805.00881 · HTML: https://mlsanity.com/paper/1805.00881/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 30 of 54)

- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments](https://mlsanity.com/paper/2401.12975/) (2024)
- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [UAV-VLN: End-to-End Vision Language guided Navigation for UAVs](https://mlsanity.com/paper/2504.21432/) (2025)
- [From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV](https://mlsanity.com/paper/2506.02649/) (2025)
- [Next-Generation LLM for UAV: From Natural Language to Autonomous Flight](https://mlsanity.com/paper/2510.21739/) (2025)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations](https://mlsanity.com/paper/2406.00887/) (2024)
- [Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control](https://mlsanity.com/paper/2103.04666/) (2021)
- [Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning](https://mlsanity.com/paper/2104.14912/) (2021)
- [Resilient UAV Swarm Communications with Graph Convolutional Neural Network](https://mlsanity.com/paper/2106.16048/) (2021)
- [Track Boosting and Synthetic Data Aided Drone Detection](https://mlsanity.com/paper/2111.12389/) (2021)
- [Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles](https://mlsanity.com/paper/2501.06205/) (2024)
- [ReS2tAC -- UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices](https://mlsanity.com/paper/2106.07927/) (2021)
- [A Search and Detection Autonomous Drone System: from Design to Implementation](https://mlsanity.com/paper/2211.15866/) (2022)
- [Diffusion Models for Multi-target Adversarial Tracking](https://mlsanity.com/paper/2307.06244/) (2023)
- [Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond](https://mlsanity.com/paper/2311.04472/) (2023)
- [Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations](https://mlsanity.com/paper/2401.07353/) (2024)
- [Learning-augmented Online Minimization of Age of Information and Transmission Costs](https://mlsanity.com/paper/2403.02573/) (2024)
- [EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV](https://mlsanity.com/paper/2403.05422/) (2024)
- [Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments](https://mlsanity.com/paper/2505.20423/) (2025)
- [Efficient UAV Trajectory-Planning using Economic Reinforcement Learning](https://mlsanity.com/paper/2103.02676/) (2021)
- [A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments](https://mlsanity.com/paper/2107.08834/) (2021)
- [UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations](https://mlsanity.com/paper/2202.10338/) (2022)
- [Sequence Models for Drone vs Bird Classification](https://mlsanity.com/paper/2207.10409/) (2022)

## Abstract (arXiv)

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that 45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.