About
With the rapid revolution and increasing availability of geospatial data not only academia, but also industry aspire for solutions to further leverage the big data and AI technologies to create new products, improve efficiencies and provide novel solutions to existing problems. However, despite the widespread interest, there is a lack of communication between the researchers in academia and industry. This limits advancements at the intersection and causes redundant or siloed efforts on both sides. Academia often has limited access to the rich and potentially useful big geospatial datasets and related real problems. In addition, the solutions proposed by the academic researchers alone are usually developed for a small scale with many assumptions, leaving a less-attended gap between methods and their applicability at scale for industrial applications. On the other hand, the industry has the data and problems at scale. However, since existing research is often not on par, industry researchers may lean towards using the traditional approaches that are developed without spatial consideration (e.g., ignoring spatial and temporal dependencies), and project teams have limited time and efforts to dive deep on the development of novel techniques that can be high-risk, but high-potential. This opens up opportunities for synergistic collaboration between industrial practitioners and academic researchers. The goal of this workshop is to offer a forum to exchange thoughts and ideas between industry and academia and reduce those siloed efforts by exploring synergies between the researchers on both sides. We envision that the collaborations, via invited and regular talks, can not only accelerate the research-to-impact cycle, but also foster workforce development for future geospatial researchers.
Organization Committee
Program Chairs
| Heba Aly (Amazon) |
| Emre Eftelioglu (Amazon) |
| Song Gao (University of Wisconsin-Madison) |
| Yan Li (Amazon) |
| Jinmeng Rao (Google DeepMind) |
| Yiqun Xie (University of Maryland) |
Program Committee
| Mingquan Chen (Google) | Jayant Gupta (Oracle) |
| Yan Li (Amazon) | Chenxi Lin (PAII Inc) |
| Hongxu Ma (Google DeepMind) | Gengchen Mai (UT Austin) |
| Guang Wang (Florida State University) | Nemin Wu (University of Georgia) |
Webmaster
| Zhihao Wang (University of Maryland) |
Schedule
TBD
Call For Papers (PDF version)
The workshop seeks high-quality regular (8-10 pages) and short (4 pages) papers that have not been published in other academic outlets and are not concurrently under peer review. Interested participants should submit a paper in the ACM format. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.
The topics include but are not limited to (in the context of industrial or related problems, such as delivery, routing, recommendation, mapping, resource allocation, and more):
Applications of AI
Big Data systems
Geospatial AI foundation models
Problems and benchmark datasets
Machine learning and deep learning
Computer vision and earth observation
Generative models and simulation
Map generation techniques
Heterogeneous data integration and analysis
Small data learning approaches
Citizen science and data collection
Spatial query processing
Spatial data management and integration
Ethical issues in geospatial data and research
Perspectives on the future of geospatial data and research
Emerging topics and trends
Important Dates
Submission deadline
August 30, 2025 (anywhere on earth)
Author notification
September 21, 2025 (anywhere on earth)
Workshop date
November 3, 2025 (ET)