DeepSpatial'21: 2nd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With the advancement of GPS and remote sensing technologies and the pervasiveness of smartphones and mobile devices, large amounts of spatiotemporal data are being collected from various domains. Knowledge discovery from spatiotemporal data is crucial in broad societal applications. Examples range from mapping flooded areas on satellite imagery for disaster response to monitoring crop health for food security, from estimating travel time between locations on Google Maps to forecasting hotspots of diseases like Covid-19 in public health. The recent success in deep learning technologies in computer vision and natural language processing provides unique opportunities for spatiotemporal data mining (e.g., automatically extracting spatial contextual features without manual feature engineering) but also faces unique challenges (e.g., spatial autocorrelation, heterogeneity, multiple scales, and resolutions, the existence of domain knowledge and constraints). This workshop provides a premium platform for researchers from both academia and industry to exchange ideas on opportunities, challenges, and cutting-edge techniques of deep learning for spatiotemporal data. We hope to inspire novel ideas and visions through the workshop and facilitate the development of this emerging research area.

Original languageEnglish (US)
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages4183-4184
Number of pages2
ISBN (Electronic)9781450383325
DOIs
StatePublished - Aug 14 2021
Externally publishedYes
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: Aug 14 2021Aug 18 2021

Publication series

NameProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period8/14/218/18/21

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

Keywords

  • data mining
  • deep learning
  • spatial-temporal data

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