English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Virtually all aspects of modern life depend on space technology. Thanks tothe great advancement of computer vision in general and deep learning-basedtechniques in particular, over the decades, the world witnessed the growing useof deep learning in solving problems for space applications, such asself-driving robot, tracers, insect-like robot on cosmos and health monitoringof spacecraft. These are just some prominent examples that has advanced spaceindustry with the help of deep learning. However, the success of deep learningmodels requires a lot of training data in order to have decent performance,while on the other hand, there are very limited amount of publicly availablespace datasets for the training of deep learning models. Currently, there is nopublic datasets for space-based object detection or instance segmentation,partly because manually annotating object segmentation masks is very timeconsuming as they require pixel-level labelling, not to mention the challengeof obtaining images from space. In this paper, we aim to fill this gap byreleasing a dataset for spacecraft detection, instance segmentation and partrecognition. The main contribution of this work is the development of thedataset using images of space stations and satellites, with rich annotationsincluding bounding boxes of spacecrafts and masks to the level of object parts,which are obtained with a mixture of automatic processes and manual efforts. Wealso provide evaluations with state-of-the-art methods in object detection andinstance segmentation as a benchmark for the dataset. The link for downloadingthe proposed dataset can be found onhttps://github.com/Yurushia1998/SatelliteDataset.

A Spacecraft Dataset for Detection, Segmentation and Parts Recognition