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 language||English (US)|
|Title of host publication||KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||2|
|State||Published - Aug 14 2021|
|Event||27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore|
Duration: Aug 14 2021 → Aug 18 2021
|Name||Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining|
|Conference||27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021|
|Period||8/14/21 → 8/18/21|
Bibliographical notePublisher Copyright:
© 2021 Owner/Author.
- data mining
- deep learning
- spatial-temporal data