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Sun 20 - Fri 25 October 2019 Athens, Greece

Global coverage and temporal resolutions of earth observation imagery data is increasing at an unprecedented rate, generating trillions of new pixels of data daily. The challenge with this ‘big data’ is finding practical ways to extract knowledge and deliver it to end users at scale, both due to the complex nature and the sheer volume of information.

Detailed, standardized geographic information is required to enable a new era of spatial temporal analytics—enabling insights to understand, monitor, and manage the earth’s resources in a sustainable manner. This can be accomplished through massive aggregation of data from remote sensors coupled with novel approaches to preparing, analyzing, and interacting with data.

Modern spatio-temporal platforms will soon be using 3D visual interactive maps with close to real-time deep learning algorithms. In addition to system infrastructure and UI/UX challenges, we also need to address the normalization problems of data, particularly with data generated from multiple sensors. Use cases in climate change and emergency response in “extreme events” would see immediate benefit from this kind of platform.

The Spatio-Temporal Observations and Knowledge on Earth Data (STOKED) workshop will provide an opportunity for researchers and stakeholders from this broad spectrum of applications and domains to start to design future platforms from an interdisciplinary perspective.

Call for Papers

The 1st International Spatio-Temporal Observations and Knowledge on Earth Data (STOKED) Workshop will address these challenges by considering preliminary research submissions in short papers (2-4 pages) and posters (1 page) for a highly interactive working group to consider topics including but not limited to:

  • data analytics and visualization
  • UI/UX
  • virtual/augmented/mixed reality
  • cloud computing
  • serverless computing
  • system infrastructure
  • machine learning
  • data formats for spatio-temporal computing
  • use cases for earth observation data
  • programming environments