# LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing

arXiv: https://arxiv.org/abs/2304.09938 · HTML: https://mlsanity.com/paper/2304.09938/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)

## Built on by (most cited first, 14 of 14)

- [How to design a dataset compliant with an ML-based system ODD?](https://mlsanity.com/paper/2406.14027/) (2024)
- [Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP](https://mlsanity.com/paper/2505.16740/) (2025)
- [LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems](https://mlsanity.com/paper/2603.26748/) (2026)
- [VerifIoU -- Robustness of Object Detection to Perturbations](https://mlsanity.com/paper/2403.08788/) (2024)
- [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)
- [Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches](https://mlsanity.com/paper/2501.18494/) (2025)
- [IoUCert: Robustness Verification for Anchor-based Object Detectors](https://mlsanity.com/paper/2603.03043/) (2026)
- [Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft](https://mlsanity.com/paper/2405.05574/) (2024)
- [Synthetic Data for Robust Runway Detection](https://mlsanity.com/paper/2510.20349/) (2025)
- [Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing](https://mlsanity.com/paper/2604.26411/) (2026)
- [Lipschitz Optimization for Formal Verification of Homographies](https://mlsanity.com/paper/2605.23203/) (2026)
- [LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection](https://mlsanity.com/paper/2607.06592/) (2026)
- [Protection Levels for Vision-Based Pose Estimation](https://mlsanity.com/paper/2608.10023/) (2026)

## Abstract (arXiv)

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages, to extend the detection task to a more realistic setting. In addition, we offer the generator which can produce such synthetic front-view images and enables automatic annotation of the runway corners through geometric transformations. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks. Find data, code and more up-to-date information at https://github.com/deel-ai/LARD