Scope
The 20th International Symposium on Spatial and Temporal Data (SSTD 2027) continues the symposium’s long and distinguished tradition as a premier forum for researchers, practitioners, and industry leaders to present and discuss innovations in spatial and temporal data management. Since its inception in 1989, SSTD has been at the forefront of advancing foundational theories, systems, and applications that address the challenges of handling spatial and temporal information.
Building on the expanded scope introduced in recent editions, SSTD 2027 embraces the full lifecycle of spatial and temporal data: from acquisition, modeling, storage, and querying to analytics, mining, visualization, and real-world deployment. The symposium particularly emphasizes the rapidly evolving intersection of spatial and temporal data with artificial intelligence, reflecting the growing importance of these data types in an era of big data, foundation models, and ubiquitous computing. We invite contributions spanning data management, analytics, mining, machine learning, visualization, and applications across diverse domains, fostering interdisciplinary discussions and accommodating emerging trends.
Call for Contributions
SSTD 2027 solicits submissions in five tracks. All tracks are single-blind: author names may appear on submissions, while reviewer names remain hidden from authors.
Research Track
Authors are invited to submit regular research papers presenting original research contributions. The selection criteria include quality, novelty, correctness, and relevance to SSTD.
Authors are also invited to submit short research papers that discuss exciting new work not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance to this category despite not having undergone extensive experimental validation or lacking a strong theoretical foundation.
Demo Track
Authors are invited to submit demo papers describing demonstrations of systems, developed or under development. The selection criteria include the novelty, the technical advances and challenges, and the overall practical attractiveness of the demonstrated system.
Industry Track
Authors are invited to submit industry papers that highlight practical applications, address real-world challenges, share original insights, and demonstrate the successful translation of research into impactful solutions for businesses and organizations.
Vision Track
Authors are invited to submit vision papers that describe revolutionary ideas likely to guide research in the near future, challenge existing assumptions prevalent in the research community, or identify novel applications and technology trends that create new research challenges.
Doctoral Student Forum
This event, which started at SSTD’25, provides a unique opportunity for researchers in spatial and temporal data science and AI who are in the early stages of their career to present their ongoing work and engage with more experienced attendees from the broader academic and industrial communities. This, in turn, will provide unique opportunities to receive constructive feedback on their current research and build collaborative relationships.
Two-Round Submission System (New for SSTD 2027)
For the first time in its history, SSTD 2027 will adopt a two-round submission system. This change is designed to give authors greater flexibility, provide earlier feedback, and accommodate the increasingly rapid pace of research in spatial and temporal data science. Authors may choose to submit to Round 1 or Round 2, or both, subject to the conditions below.
Round 1 — Early Submissions (Only Research Track)
- Submission deadline: December 6, 2026 (AoE)
- Papers accepted in Round 1 will be considered for inclusion in the SSTD 2027 proceedings and will have the opportunity to be revised before the camera-ready deadline.
- Authors of rejected papers in Round 1 may resubmit an improved version in Round 2.
Round 2 — Regular Submissions (All Tracks)
- Submission deadline: April 11, 2027 (AoE)
- Round 2 is open to all authors, including those who did not submit in Round 1 and those resubmitting revised work following Round 1 decisions. Papers recommended in Round 1 for major revision are reviewed by the same PC members, whereas rejected papers will be considered as new submissions.
- All papers accepted across both rounds will be published in the SSTD 2027 proceedings by ACM.
Papers submitted to Round 1 that are not accepted may be resubmitted to Round 2 with improvements, provided they comply with the standard originality and dual-submission policies. Papers accepted in either round are expected to be presented at the conference.
Topics of Interest
Topics of interest include, but are not limited to, the following in the context of spatial, temporal, and spatio-temporal data.
Spatial Foundations
- Spatial and temporal data models, representations, and standards
- Computational geometry algorithms
- Theoretical frameworks and complexity analysis
- Spatial approximation, sampling, and compression strategies
- Topological, geometric, and semantic relationships
Data Management and Storage
- Data types and query languages
- Database design and conceptual modeling
- Storage and retrieval techniques
- Access methods and indexing
- Query processing and optimization
- Parallel and distributed databases
- Distributed and cloud-based architectures
- Data streams and stream processing
- Real-time indexing
- Moving objects
- Methods for data integration and harmonization
- Similarity search
- Uncertain, imprecise, and probabilistic data
- New hardware platforms
Spatial Data Mining, Machine Learning, and Analytics
- Data mining and knowledge discovery
- Data warehousing and decision support
- Pattern recognition, clustering, and outlier detection
- Predictive modeling and forecasting
- Recommender systems
- Semantic data techniques and reasoning
Spatial Statistical Analysis
- Spatial autocorrelation and regression models
- Spatial point process analysis
- Geostatistical methods
- Bayesian models
- Analysis of spatial heterogeneity
- Spatio-temporal statistical methods
Spatial Networks and Graphs
- Transportation and mobility analytics
- Network modeling and route optimization
- Graph modeling and algorithms
- Map matching
- Sensor networks
Geographic Information Retrieval (GIR)
- Spatially-aware search engines
- Geotagging and geographic text analysis
- Location-based information retrieval
- Integration of spatial and textual data
- Spatio-textual techniques
- Web information retrieval
- Ontologies and taxonomies
- Semantic analysis of geographic entities
- Spatial-keyword search
Visualization and Interaction
- Novel visualization techniques
- User interfaces and visualization
- Human-computer interaction
- AR/VR environments and immersive visualizations
Quality, Uncertainty, Fairness, and Privacy
- Data quality assessment and cleaning
- Handling uncertainty and incomplete data
- Privacy-preserving methods
- Privacy, security, and integrity
- Data bias, fairness, accountability, and transparency
Applications and Emerging Domains
- Location-based services
- Location-based social networks
- Experiments, benchmarks, and performance evaluation
- Smart cities and urban planning
- Public health and emergency management
- IoT-based sensor networks
- Computer vision for spatial data
- Crowdsourcing and user-generated content
- Indoor spaces and indoor mobility
- Spatial quantum modeling
- Real applications and systems
AI for Spatial and Spatiotemporal Data
- Foundation models and large language models (LLMs) for spatial and spatio-temporal data
- Deep learning and Geo-AI techniques for spatial analysis
- Self-supervised and contrastive learning for spatial representations
- Graph neural networks for spatio-temporal data
- Generative AI for spatial data synthesis and augmentation
- Diffusion models for spatio-temporal forecasting and simulation
- Reinforcement learning for spatial decision-making and mobility
- Explainable AI (XAI) for spatial and spatio-temporal models
- Transfer learning and domain adaptation in geospatial contexts
- AI-driven geosimulation and digital twins
- Multi-modal learning integrating spatial, temporal, textual, and visual data
- Uncertainty quantification in AI models for spatial data
Spatial and Spatiotemporal Data for AI
- AI-ready spatial and temporal data
- Spatio-temporal data as a foundation for training and evaluating AI systems
- Benchmarking and datasets for spatial AI and spatio-temporal machine learning
- Spatial data curation, annotation, and quality assurance for AI pipelines
- Spatio-temporal data infrastructures and architectures supporting AI workloads
- Responsible and ethical AI enabled by spatial and temporal data
- Federated learning and privacy-preserving AI over distributed spatial data
- Spatial data governance, provenance, and lineage for AI reproducibility
- Real-time spatio-temporal data streams for online and continual learning
- Human-in-the-loop AI systems leveraging spatial and temporal context
- Vector databases and retrieval-augmented generation (RAG) for spatial data
- Scalable data management for large-scale AI training on spatio-temporal corpora
- Evaluating fairness, bias, and robustness of AI using spatial and temporal data
Important Dates
| Milestone | Date |
|---|---|
| Round 1 — Abstract submission | November 29, 2026 (AoE) |
| Round 1 — Paper submission deadline | December 6, 2026 (AoE) |
| Round 1 — Notification | January 2027 (TBD) |
| Round 2 — Abstract submission | April 4, 2027 (AoE) |
| Round 2 — Paper submission deadline | April 11, 2027 (AoE) |
| Round 2 — Notification | June 2027 (TBD) |
| Thesis and Dissertation Workshop track submission deadline | June 2027 (TBD) |
| Camera-ready | July 2027 (TBD) |
| Conference | August 2027 (TBD) |
All deadlines refer to 23:59 in the AoE (Anywhere on Earth) time zone.
Journal Special Issue
Outstanding research papers will be selected for an extended journal submission to a special track of “SSTD 2027 Best Papers” in GeoInformatica (Springer) and/or ACM Transactions on Spatial Algorithms and Systems (TSAS). One paper will be selected for the “SSTD’s Ralf H. Güting Best Paper Award.”
Submission Instructions
Submissions should be formatted using the double-column ACM SIG Conference Proceedings template.
If you use Overleaf, you can access the ACM official template. This template requires switching the document class from “manuscript” to “sigconf” to ensure the required double-column format.
Submissions cannot have been or be submitted to another venue while being reviewed at SSTD and must have significant new content compared to previous publications.
| Track | Page Limit |
|---|---|
| Regular Research Papers | 10 pages (excluding references) |
| Industry Papers | 10 pages (excluding references) |
| Short Research Papers | 4 pages (excluding references) |
| Demo Papers | 4 pages (excluding references) |
| Vision Papers | 4 pages (excluding references) |
Papers should be submitted via the CMT submission system (link TBD) to the appropriate track and round. The review process is single-blind: reviewers are not identified, while author names are listed in the submission.
All accepted papers will be published in the SSTD 2027 proceedings by ACM.
The fair use of AI tools is permitted and must comply with the ACM policies regarding AI tools in authorship and the peer-reviewing process. For more information, please see https://www.acm.org/publications/policies.
Important Note to Authors About the ACM Open Access Publishing Model
ACM has introduced a new open access publishing model for the International Conference Proceedings Series (ICPS). Authors based at institutions that are not yet part of the ACM Open program and do not qualify for a waiver will be required to pay an article processing charge (APC) to publish their ICPS article in the ACM Digital Library. To determine whether an APC will be applicable to your article, please follow the detailed guidance here.
Further information may be found on the ACM website:
Please direct all questions about the new model to icps-info@acm.org.
Contact
For questions regarding submissions, please contact the Program Committee Chairs at sstd27oc@gmail.com.
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.