@inproceedings {naseer2020:deep,
	title = {Deep neural architecture for geospatial trajectory completion over occupancy gridmap},
	journal = {2020 IEEE 13th International Conference on Cloud Computing (CLOUD)2020 IEEE 13th International Conference on Cloud Computing (CLOUD)},
	year = {2020},
	month = {10/2020},
	pages = {37-39},
	publisher = {IEEE},
	address = {Beijing, China},
	abstract = {GPS data is widely used in many real-world applications. The quality of GPS data is critically important to produce high-quality results. In real-world applications, certain GPS trajectories are sparse and incomplete, which causes challenges to GPS trajectory-based applications. Few existing studies have tried to address this problem using complicated algorithms based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Deep learning in the recent era has achieved great success in solving many sequences to sequence prediction problems. In this paper, deep learning-based bidirectional convolutional recurrent encoder-decoder architecture using an attention mechanism is proposed that predicts the missing data points, resulting in a complete GPS trajectory. The proposed method shows significant improvement over state-of-the-art benchmark methods.},
	keywords = {deep learning, GPS, trajectories},
	isbn = {978-1-7281-8780-8},
	issn = {2159-6190},
	doi = {10.1109/CLOUD49709.202010.1109/CLOUD49709.2020.00018},
	url = {https://ieeexplore.ieee.org/document/9284256},
	author = {Nawaz, Asif and Huang, Zhiqiu and Wang, Senzhang and Naseer, Amara},
	editor = {O{\textquoteright}Conner, Lisa}
}
