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

This repository is the data release of  our paper Deep learning-assisted near-Earth asteroid tracking in astronomical images. There are two categories in this repository:

Simulated training dataset for training the star segmentation network. 

The dataset consists of two folders: image (grayscale images) and mask (binary images). The size of each image is 256*256.

Example data for testing asteroid tracking algorithm.

If you find this work useful, please cite our paper:

@article{du2024ASR,

title = {Deep learning-assisted near-Earth asteroid tracking in astronomical images},

journal = {Advances in Space Research},

volume = {73},

number = {10},

pages = {5349-5362},

year = {2024},

issn = {0273-1177},

doi = {https://doi.org/10.1016/j.asr.2024.02.048},

url = {https://www.sciencedirect.com/science/article/pii/S0273117724001911},

author = {Zhenhong Du and Hai Jiang and Xu Yang and Hao-Wen Cheng and Jing Liu},

keywords = {Near-Earth asteroid, Deep learning, Convolutional neural network, Faint object extraction, Moving object linking},

}