IEEE ICDM 18th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM)
December 9, 2024
Abu Dhabi National Exhibition Centre, UAE.


Call for Papers

With advances in remote sensors, sensor networks, and the proliferation of location-sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. Advances in ground, air-, and space-borne sensor technologies provide unprecedented access to Earth science data. However, the rapid generation of geospatial data exceeds our ability to analyze it effectively. This workshop explores Geospatial AI, Machine Learning, and Spatiotemporal Computing technologies to address challenges and offer innovative solutions for various problems, including climate change, sea-level rise, food, energy, water, natural disasters, and other critical issues.

Innovative, efficient, and explainable AI/ML techniques are needed to extract value from massive, complex geospatial data. Traditional methods fall short due to their failure to account for spatial and temporal complexities. We invite researchers and practitioners to submit original papers that explore new approaches, share insights, and address challenges in geospatial AI and spatiotemporal data mining.

Topics of interest include, but are not limited to, the following:
* Theoretical foundations of geo-spatial-temporal DM, AI, ML, and DL
* Recent advances in Deep Learning for Spatial and Spatiotemporal Big Data
* Spatial and spatiotemporal analogs of interesting patterns: frequent itemsets, clusters, outliers, and the algorithms to
mine them
* Advances in Unsupervised, Supervised, Semi-supervised, Self-supervised, Transfer, and Active learning for spatial and
spatiotemporal data
* Methods that explicitly model spatial and temporal context
* Spatial and spatiotemporal autocorrelation and heterogeneity, its quantification, and efficient incorporation into the ML
and DM algorithms
* Image (multispectral, hyperspectral, aerial, radar) information mining, change detection
* Role of uncertainty in spatial and spatiotemporal data mining
* Integrated approaches to multi-source and multimodal data mining
* Resource-aware techniques to mine streaming spatiotemporal data
* Spatial and spatiotemporal data mining at multiple granularities (space and time)
* Data structures and indexing methods for spatiotemporal data mining
* Spatial and Spatiotemporal online analytical processing and data warehousing
* Geospatial Intelligence
* High-performance SSTDM
* Spatiotemporal data mining at the edge
* Novel applications that demonstrate success stories of spatial and spatiotemporal data mining (e.g., Climate Change,
Sea level rise, Natural Hazards, Critical Infrastructures)
* Spatiotemporal data mining for Agriculture, Energy, Water, Forestry, and Natural Resources
* Spatiotemporal data mining for detecting processes on and in the polar ice sheets, and attributing their changes to
climate variability and change
* Harness big, heterogeneous, and discontinuous spatiotemporal data coupled with physics models to improve our
understanding of polar ice dynamics
* Spatiotemporal Data Mining for Epidemiology and Health
* Spatiotemporal Data Mining for Social Good
* Spatiotemporal benchmark datasets

Important Dates
* August 31, 2024: Paper submission
* October 7, 2024: Acceptance notification
* October 11, 2024: Camera-ready deadline and copyright form
* December 9, 2024: In-person workshop

Paper Submissions
This is an open call for papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 4-5
pages) describing work-in-progress or case studies. Only original and high-quality papers formatted using the IEEE 2-column
format (Latex Template), including the bibliography and any possible appendices, will be considered for review.

Proceedings
All submitted papers will be evaluated by 2-3 program committee members, and accepted papers will be included in an ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press and will be included in the IEEE Xplore Digital Library.
Contact: Send Email To (stac.lab.raju@gmail.com)