# Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Products

arXiv: https://arxiv.org/abs/2307.07681 · HTML: https://mlsanity.com/paper/2307.07681/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Autonomous Vehicles Meet the Physical World: RSS, Variability, Uncertainty, and Proving Safety (Expanded Version)](https://mlsanity.com/paper/1911.01207/) (2019)

## Built on by (most cited first, 4 of 4)

- [Implementation of airborne ML models with semantics preservation](https://mlsanity.com/paper/2509.18681/) (2025)
- [Defining Operational Conditions for Safety-Critical AI-Based Systems from Data](https://mlsanity.com/paper/2601.22118/) (2026)
- [Certified ML Object Detection for Surveillance Missions](https://mlsanity.com/paper/2406.12362/) (2024)
- [Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing](https://mlsanity.com/paper/2604.26411/) (2026)

## Abstract (arXiv)

We give a first rigorous characterization of Operational Design Domains (ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, together with a categorization of the data that ML-based applications can encounter in operation, whilst identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: the requirements necessary to drive the design of ML Models (MLMs); the potential effects on MLMs and higher levels of the system hierarchy; the learning assurance processes that may be needed, and system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope.