IEEE ICDM 18th International Workshop on
Spatial and Spatiotemporal Data Mining (SSTDM)
December 1, 2023
Shanghai International Convention Center, China. (Hybrid)
What this workshop is about?
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. In addition, significant progress in the ground, air- and
space-borne sensor technologies has led to unprecedented access to earth science data, including polar data,
for scientists from different disciplines, interested in studying the complementary nature of different parameters.
These developments are quickly leading toward a data-rich but information-poor environment. The rate at which g
eospatial data are being generated clearly exceeds our ability to organize and analyze them to extract patterns
critical for understanding in a timely manner a dynamically changing world. Access to such data can help address
important challenges such as climate change, sea-level rise, and their impact on communities through transformative
spatiotemporal data science and machine learning. This workshop focuses on advances at the intersection of
Geospatial AI, Machine Learning, and Spatiotemporal Computing in order to address these scientific and computational
challenges and provide innovative and effective solutions.
More specifically, efficient, reliable, and explainable AI, Machine Learning, and Data Mining techniques are
needed for extracting useful geoinformation from large heterogeneous, often multi-modal spatiotemporal
datasets (e.g., remote sensing, GIS, trajectory, geo-social media). Traditional techniques are ineffective
as they do not incorporate the idiosyncrasies of the spatial domain, which include (but are not limited to)
spatial autocorrelation, spatial context, and spatial constraints. Extracting useful geoinformation and
actionable knowledge from several terabytes of streaming multi-modal data per day also demands the use of
modern computing in all its forms (clusters to the cloud). Thus, we invite all researchers and practitioners
to participate in this event and share, contribute, and discuss the emerging challenges in Geo-spatial-temporal AI,
Machine Learning, and Data Mining. .
Organizers
