SSTDM-2024

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 a range of 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 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 analogues 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

Sept. 10 22, 2024: Paper submission
October 7, 2024: Acceptance notification
October 11, 2024: Camera-ready deadline and copyright form
December 9, 2024: Workshop date ()

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 reviewing.

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. Check workshop website for sumbission instructions.