Building on previous successful DeepSpatial’20, DeepSpatial’21, and DeepSpatial’22 workshops at SIGKDD, we are proposing the 4th SIGKDD Workshop on Deep Learning for Spatio-temporal Data, Applications, and Systems (DeepSpatial’24) at the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining as a half-day workshop. This year’s workshop features several new items, including emerging themes related to foundation models for spatiotemporal data and their interdisciplinary scientific applications, two distinguished keynote speakers, and two discussion panels on ongoing debates in the field. This workshop will provide a premium platform for researchers from both academia and industry to exchange ideas on emerging research themes related to deep learning for spatiotemporal data, particularly on Foundation Models (LLMs, LVMs) and their interdisciplinary scientific applications.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full papers: up to 9 pages (8 pages at most for the main body and the last page can only hold references)
• Posters: up to 5 pages (4 pages at most for the main body and the last page can only hold references)
• Extended abstracts (posters): up to 2 pages

List of Topics

• Emerging Foundation Models and Deep Learning for Spatiotemporal Data
• Spatial Representation and Networks
• Responsible GeoAI and Applications
• Benchmarking and Optimization

Committees

Organizing committee

• Zhe Jiang, University of Florida
• Liang Zhao, Emory University
• Xun Zhou, Harbin Institute of Technology, Shenzhen
• Junbo Zhang, JD Intelligent Cities Research
• Jieping Ye, Alibaba Group
• Shashi Shekhar, University of Minnesota 

Contact

All questions about submissions should be emailed to Zhe Jiang, zhe.jiang@ufl.edu, Liang Zhao, liang.zhao@emory.edu, and Xun Zhou, zhouxun2023@hit.edu.cn.