# AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service

arXiv: https://arxiv.org/abs/2504.03802 · HTML: https://mlsanity.com/paper/2504.03802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2025.

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

- [AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing](https://mlsanity.com/paper/2510.23053/) (2025)
- [AeroGen: Agentic Drone Autonomy through Single-Shot Structured Prompting & Drone SDK](https://mlsanity.com/paper/2603.14236/) (2026)

## Abstract (arXiv)

The increasing adoption of UAVs with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in fields such as precision agriculture, wildfire monitoring, and environmental conservation. However, integrating deep learning on UAVs remains challenging due to platform heterogeneity, real-time constraints, and the need for seamless cloud-edge coordination. To address these challenges, we introduce AeroDaaS, a service-oriented framework that abstracts UAV-based sensing complexities and provides a Drone-as-a-Service (DaaS) model for intelligent decision-making. AeroDaaS offers modular service primitives for on-demand UAV sensing, navigation, and analytics as composable microservices, ensuring cross-platform compatibility and scalability across heterogeneous UAV and edge-cloud infrastructures. We implement and evaluate a preliminary version of AeroDaaS for two real-world DaaS applications. We require <=40 lines of code for the applications and see minimal platform overhead of <=20 ms per frame and <=0.5 GB memory usage on Orin Nano. These early results are promising for AeroDaaS as an efficient, flexible and scalable UAV programming framework for autonomous aerial analytics.