AERO-DETR Runway Dataset: A Georeferenced Benchmark for Runway Detection and Marking Recognition in High-Resolution Aerial Imagery

The AERO-DETR Runway Dataset is a georeferenced benchmark for runway detection and marking recognition from sub-meter aerial imagery. It covers 125 U.S. airports across five runway configuration categories (single, parallel, intersection, mixed, complex) with imagery from public-domain NAIP and NOAA archives at 0.42–0.72 m GSD. The dataset provides dual-level annotations: 1,038 oriented bounding boxes (OBBs) delineating individual runways, and 12,816 fine-grained marking polygons across eight classes aligned with RTCA DO-272 and FAA AC 150/5340-1M. Annotations are provided in LabelMe JSON format across 433 full-scene images and 995 orientation-normalized runway crops. The deposit includes georeferenced imagery (JPEG 2000), vector annotations, per-airport metadata, and reproducible Python pipelines for dataset creation and format conversion. See README.md for full documentation.AERO-DETR跑道数据集是一款具备地理参考属性的基准数据集，旨在从亚米级航空影像中开展跑道检测与标志识别任务。该数据集涵盖美国125座机场，覆盖5类跑道构型（单跑道、平行跑道、交叉跑道、混合构型跑道、复杂构型跑道），影像取自公有领域的NAIP与NOAA档案，地面采样距离（Ground Sample Distance, GSD）为0.42–0.72米。 该数据集提供双层级标注：1038个定向边界框（Oriented Bounding Boxes, OBBs）用于勾勒单条跑道轮廓，以及12816个细粒度标志多边形，共分为8个类别，符合RTCA DO-272标准与美国联邦航空管理局（Federal Aviation Administration, FAA）AC 150/5340-1M规范。标注文件以LabelMe JSON格式提供，涵盖433张全场景影像与995张经方向归一化处理的跑道裁剪图像。 数据集存档包含地理参考影像（JPEG 2000格式）、矢量标注、单机场元数据，以及用于数据集构建与格式转换的可复现Python处理流水线。完整文档请参阅README.md文件。