# How to design a dataset compliant with an ML-based system ODD?

arXiv: https://arxiv.org/abs/2406.14027 · HTML: https://mlsanity.com/paper/2406.14027/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [CRAFT: Concept Recursive Activation FacTorization for Explainability](https://mlsanity.com/paper/2211.10154/) (2022)
- [LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing](https://mlsanity.com/paper/2304.09938/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance](https://mlsanity.com/paper/2503.04739/) (2025)
- [LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems](https://mlsanity.com/paper/2603.26748/) (2026)
- [Defining Operational Conditions for Safety-Critical AI-Based Systems from Data](https://mlsanity.com/paper/2601.22118/) (2026)
- [Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing](https://mlsanity.com/paper/2604.26411/) (2026)
- [On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks](https://mlsanity.com/paper/2608.20053/) (2026)
- [Robust Validation to Geometric Perturbations for Autonomous Pose Estimation](https://mlsanity.com/paper/2608.21066/) (2026)

## Abstract (arXiv)

This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications.