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}, }