skip to main content
10.1145/3534678.3542905acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
abstract

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

Published: 14 August 2022 Publication History

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.

Index Terms

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

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
        August 2022
        5033 pages
        ISBN:9781450393850
        DOI:10.1145/3534678
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 14 August 2022

        Check for updates

        Author Tags

        1. data mining
        2. deep learning
        3. spatiotemporal data

        Qualifiers

        • Abstract

        Conference

        KDD '22
        Sponsor:

        Acceptance Rates

        Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

        Upcoming Conference

        KDD '25

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 144
          Total Downloads
        • Downloads (Last 12 months)24
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 01 Mar 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media