# LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems

arXiv: https://arxiv.org/abs/2603.26748 · HTML: https://mlsanity.com/paper/2603.26748/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing](https://mlsanity.com/paper/2304.09938/) (2023)
- [How to design a dataset compliant with an ML-based system ODD?](https://mlsanity.com/paper/2406.14027/) (2024)
- [Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features](https://mlsanity.com/paper/2407.16223/) (2024)
- [Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators](https://mlsanity.com/paper/2409.00252/) (2024)
- [Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP](https://mlsanity.com/paper/2505.16740/) (2025)
- [BARS: A Benchmark for Airport Runway Segmentation](https://mlsanity.com/paper/2210.12922/) (2022)
- [Federated Adversarial Learning for Robust Autonomous Landing Runway Detection](https://mlsanity.com/paper/2406.15925/) (2024)
- [Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System](https://mlsanity.com/paper/2508.09732/) (2025)

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

- [Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System](https://mlsanity.com/paper/2605.20607/) (2026)
- [Certified geometric robustness -- Super-DeepG](https://mlsanity.com/paper/2604.24379/) (2026)
- [LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection](https://mlsanity.com/paper/2607.06592/) (2026)
- [On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks](https://mlsanity.com/paper/2608.20053/) (2026)

## Abstract (arXiv)

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.