Machine Learning Challenges in Intelligent Unmanned Aerial Vehicle Operations in Developing Economie
This is a survey/opinion piece that outlines known challenges for practitioners working on UAVs in developing economies, but does not present new results or solutions.
The paper identifies and discusses the amplified challenges of applying machine learning to UAV operations in developing economies, such as cost sensitivity, limited infrastructure, and regulatory uncertainty, highlighting the gap between controlled experiments and real-world deployment.
Unmanned aerial vehicle (UAV) environments present significant challenges for machine learning (ML) due to limited platform resources, heterogeneous sensor data, dynamic mission conditions, and safety-critical requirements. This paper examines these constraints across the core functional areas of UAV intelligence, including navigation, perception, communication-aware operation, and resilience specifically in the context of developing economies. In such settings, these challenges are often amplified by constraints such as cost sensitivity, limited infrastructure, intermittent connectivity, regulatory uncertainty, and harsh or variable operating environments. The discussion highlights the gap between ML performance in controlled experimental backgrounds and dependable deployment in real-world UAV missions within developing economies context.