mlsanityData to 7 Oct 2026

LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems

arXiv 2603.26748 ↗First posted 23 Mar 2026cs.RO cs.AIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 23 Mar 2026.

Builds on

8 of 27 references.

References · 8

  1. 2023LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landingcited by 14
  2. 2024How to design a dataset compliant with an ML-based system ODD?cited by 6
  3. 2024Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Featurescited by 5
  4. 2024Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creatorscited by 5
  5. 2025Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAPcited by 5
  6. 2022BARS: A Benchmark for Airport Runway Segmentationcited by 4
  7. 2024Federated Adversarial Learning for Robust Autonomous Landing Runway Detectioncited by 3
  8. 2025Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing Systemcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing Systemcited by 1
  2. 2026Certified geometric robustness -- Super-DeepGcited by 0
  3. 2026LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detectioncited by 0
  4. 2026On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networkscited by 0