DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems

Zhe Jiang; Liang Zhao; Xun Zhou; Robert N. Stewart; Junbo Zhang; Shashi Shekhar; Jieping Ye

doi:10.1145/3534678.3542905

DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems

Zhe Jiang, Liang Zhao, Xun Zhou, Robert N. Stewart, Junbo Zhang, Shashi Shekhar, Jieping Ye

Spatial Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With the advancement of GPS and remote sensing technologies and the pervasiveness of smartphones and IoT devices, an enormous amount of spatiotemporal data are being collected from various domains. Knowledge discovery from spatiotemporal data is crucial in addressing many grand societal challenges, ranging from flood disaster management to monitoring coastal hazards, and from autonomous driving to disease forecasting. The recent success in deep learning technologies in computer vision and natural language processing provides new opportunities for spatiotemporal data mining, but existing deep learning techniques also face unique spatiotemporal challenges (e.g., autocorrelation, non-stationarity, physics awareness). This workshop provides a premium platform for researchers from both academia and industry to exchange ideas on the opportunities, challenges, and cutting-edge techniques related to deep learning for spatiotemporal data.

Original language	English
Title of host publication	KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4878-4879
Number of pages	2
ISBN (Electronic)	9781450393850
DOIs	https://doi.org/10.1145/3534678.3542905
State	Published - Aug 14 2022
Event	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States Duration: Aug 14 2022 → Aug 18 2022

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/Territory	United States
City	Washington
Period	08/14/22 → 08/18/22

Bibliographical note

Publisher Copyright:
© 2022 Owner/Author.

Keywords

data mining
deep learning
spatiotemporal data

Access to Document

10.1145/3534678.3542905

Cite this

Jiang, Z., Zhao, L., Zhou, X., Stewart, R. N., Zhang, J., Shekhar, S., & Ye, J. (2022). DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4878-4879). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3534678.3542905

Jiang, Zhe ; Zhao, Liang ; Zhou, Xun et al. / DeepSpatial'22 : The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. pp. 4878-4879 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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abstract = "With the advancement of GPS and remote sensing technologies and the pervasiveness of smartphones and IoT devices, an enormous amount of spatiotemporal data are being collected from various domains. Knowledge discovery from spatiotemporal data is crucial in addressing many grand societal challenges, ranging from flood disaster management to monitoring coastal hazards, and from autonomous driving to disease forecasting. The recent success in deep learning technologies in computer vision and natural language processing provides new opportunities for spatiotemporal data mining, but existing deep learning techniques also face unique spatiotemporal challenges (e.g., autocorrelation, non-stationarity, physics awareness). This workshop provides a premium platform for researchers from both academia and industry to exchange ideas on the opportunities, challenges, and cutting-edge techniques related to deep learning for spatiotemporal data.",

keywords = "data mining, deep learning, spatiotemporal data",

author = "Zhe Jiang and Liang Zhao and Xun Zhou and Stewart, {Robert N.} and Junbo Zhang and Shashi Shekhar and Jieping Ye",

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month = aug,

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doi = "10.1145/3534678.3542905",

language = "English",

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Jiang, Z, Zhao, L, Zhou, X, Stewart, RN, Zhang, J, Shekhar, S & Ye, J 2022, DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. in KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 4878-4879, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, United States, 08/14/22. https://doi.org/10.1145/3534678.3542905

DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. / Jiang, Zhe; Zhao, Liang; Zhou, Xun et al.
KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. p. 4878-4879 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Jiang Z, Zhao L, Zhou X, Stewart RN, Zhang J, Shekhar S et al. DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2022. p. 4878-4879. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3534678.3542905

DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this