Data from: Deep learning-assisted near-Earth asteroid tracking in astronomical images

<p>This repository is the data release of&nbsp; our paper <em>Deep learning-assisted near-Earth asteroid tracking in astronomical images</em>. There are two categories in this repository:</p> <ul> <li>Simulated training dataset for training the star segmentation network.&nbsp;</li> </ul> <p>The dataset consists of two folders: image (grayscale images) and mask (binary images). The size of each image is 256*256.</p> <ul> <li>Example data for testing asteroid tracking algorithm.<br><br></li> </ul> <p>If you find this work useful, please cite our paper:</p> <div> <div>@article{du2024ASR,</div> <div>title = {Deep learning-assisted near-Earth asteroid tracking in astronomical images},</div> <div>journal = {Advances in Space Research},</div> <div>volume = {73},</div> <div>number = {10},</div> <div>pages = {5349-5362},</div> <div>year = {2024},</div> <div>issn = {0273-1177},</div> <div>doi = {https://doi.org/10.1016/j.asr.2024.02.048},</div> <div>url = {https://www.sciencedirect.com/science/article/pii/S0273117724001911},</div> <div>author = {Zhenhong Du and Hai Jiang and Xu Yang and Hao-Wen Cheng and Jing Liu},</div> <div>keywords = {Near-Earth asteroid, Deep learning, Convolutional neural network, Faint object extraction, Moving object linking},</div> <div>}</div> </div>