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

翻译：随着对自主系统兴趣的持续增长，收集充足且具有代表性的真实世界数据成为主要挑战之一。尽管航空航天领域的自主着陆系统具有强烈的实用和商业兴趣，但公开的航空图像数据集仍然匮乏。为解决这一问题，我们提出一个高质量航空图像数据集LARD，用于进近和着陆阶段的跑道检测任务。该数据集主要由合成图像构成，同时包含来自真实着陆视频的人工标注图像，以将检测任务扩展到更实际的场景中。此外，我们还提供了生成器，可产生此类合成前视图像，并通过几何变换自动标注跑道角点。该数据集为后续研究（如数据集质量分析或应对检测任务的模型开发）铺平了道路。相关数据、代码及最新信息请参见 https://github.com/deel-ai/LARD