Abstract
The "Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation" dataset here published should be used for Region of Interest (ROI) and/or semantic segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to ROI extraction and Semantic Segmentation tasks (up to our knowledge). The label of each image gives, for the Bounding Box annotations, the filename of the image, the ROI top-left corner (minimum x, minimum y) in pixels, the ROI bottom-right corner (maximum x, maximum y) in pixels, and the center point of the ROI in pixels. The annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward. Concerning the Semantic Segmentation, RGB masks are provided. Each RGB mask correspond to a single image in both train and test dataset. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). More information on the dataset split and on the label format are reported below. Images Information: The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.
Paolo Lunghi
Assistant Professor of Aerospace Systems
Aiming for autonomous Guidance, Navigation, and Control for spacecraft.