Authors marked with an asterisk (*) have contributed equally to this publication. We annotated a dataset for the detection of drainage outlets and ventilations on flat rooftops. The underlying high-resolution aerial images are orthophotos with a ground sampling distance of 7.5 cm, provided by the Office for Land Management and Geoinformation of the City of Bonn, Germany. The dataset was created through manual annotation using the Computer Vision Annotation Tool (CVAT) and comprises 740 image pairs. Each pair consists of a rooftop image and a corresponding annotated mask indicating the drainage outlets and ventilations. Since rooftops vary in size, we aimed to create image pairs that capture a single rooftop per image without overlaps or cutoffs. Consequently, the dimensions of each image pair differ. The dataset is split randomly into 80% for training, 10% for validation, and 10% for testing. We provide the dataset in the Common Objects in Context (COCO) format for object detection tasks. In addition to the COCO-formatted dataset, we provide the dataset in its original, pairwise, format to support various machine learning tasks, such as semantic segmentation and panoptic segmentation, as well as to accommodate different data-loading requirements for diverse deep learning models. If your object detection approach requires the 'category_id' to start from 0 instead of 1, please refer to the following guide: https://github.com/obss/sahi/discussions/336For conversion to a completely different dataset format, such as YOLO, please see the repository: https://github.com/ultralytics/JSON2YOLO