The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Urban big data fusion based on deep learning: An overview
Abstract
Abstract Urban big data fusion creates huge values for urban computing in solving urban problems. In recent years, various models and algorithms based on deep learning have been proposed to unlock the power of knowledge from urban big data. To clarify the methodologies of urban big data fusion based on deep learning (DL), this paper classifies them into three categories: DL-output-based fusion, DL-input-based fusion and DL-double-stage-based fusion. These methods use deep learning to learn feature representation from multi-source big data. Then each category of fusion methods is introduced and some examples are shown. The difficulties and ideas of dealing with urban big data will also be discussed.
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How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Ye, J., Zhao, J., Ye, K., & Xu, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3043250 · Cited by 281
Multimodal data fusion for systems improvement: A review
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IoT-based platform for environment data sharing in smart cities
Rubí, J. N. S., & de Lira Gondim, P. R. (2021) · International Journal of Communication Systems · doi:10.1002/dac.4515 · Cited by 36
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N., Tenhunen, H., Gabbouj, M., Raitoharju, J., & Westerlund, T. (2020) · arXiv · doi:10.48550/arxiv.2008.12610 · Cited by 27
Metadata
- Authors
- Liu, J., Li, T., Xie, P., Du, S., Teng, F., & Yang, X.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 53
- Pages
- 123-133
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2952613166
- Cited by
- 266
- References
- 76
- FWCI
- 19.69
- Type
- journal article
- Language
- en
- Open access
- closed
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