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UAS Orchestration Engine

This project provides an open-source orchestration engine designed to automate and scale the transformation of raw Unmanned Aerial Systems (UAS) imagery into structured, AI-ready datasets for agricultural research.

GitHub Repo License

References​

Paper: An Orchestration Engine for Scalable, On-Demand AI Phenotyping from UAS Imagery in Agriculture Lucas Waltz (The Ohio State University), Sarikaa Sridhar (The Ohio State University), Ryan Waltz (The Ohio State University), Paul Rodriguez (University of California, San Diego), Chaeun Hong (The Ohio State University), Armeen Ghoorkhanian (The Ohio State University), Nicole DiMarco (The Ohio State University) Raghu Machiraju (The Ohio State University), and Sami Khanal (The Ohio State University). Proceedings of the 33rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '25)

Project Website: https://go.osu.edu/aerialagriviewer_sigspatial2025

Live Demo: https://go.osu.edu/aerialagriviewer_sigspatial2025

Demo Dataset: https://app.globus.org/file-manager?origin_id=87b3fbdc-2d9d-4fad-93ca-e3828ae2f37d&origin_path=%2F

Note: The demo dataset includes a 2025_poc/stashed/ folder containing reference outputs from the orchestration engine. You can use these outputs to validate your pipeline results when running the orchestration engine

Acknowledgements​

National Science Foundation (NSF) funded AI institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) (OAC 2112606)

Issue reporting​

Contact:

For questions or support:

Luke Waltz: waltz.12@osu.edu