# Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions

arXiv: https://arxiv.org/abs/2006.14544 · HTML: https://mlsanity.com/paper/2006.14544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations](https://mlsanity.com/paper/2401.07353/) (2024)
- [Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet](https://mlsanity.com/paper/2311.03221/) (2023)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers and central data bases via a communications network. This paper discusses the challenges and opportunities for machine learning (ML) for effectively providing critical UTM services. We introduce the four pillars of UTM---operation planning, situational awareness, status and advisors and security---and discuss the main services, specific opportunities for ML and the ongoing research. We conclude that the multi-faceted operating environment and operational parameters will benefit from collected data and data-driven algorithms, as well as online learning to face new UAV operation situations.