cloud

Towards a Cloud-Native Geospatial standards baseline

  • Posted on: 3 December 2021
Contributing Author Information:
Contributed by: 
Chris Holmes, OGC Visiting Fellow

Photo of clouds overlaid with the text 'Towards a Cloud Native OGC - Part 2: The Required Standards'In his previous post, Chris Holmes laid out the vision for Cloud-Native Geospatial. With this next post, he gets into the details of what is needed, laying out the key areas where foundational standards are needed, and then surveying the current status of each area. 
The existing technologies and Standards range from quite well-established to quite speculative, but all are eminently achievable. Chris then takes a 'deep dive' into the area that he ended up focusing on the most during his time as OGC Visiting Fellow these last few months.

Towards a Cloud-Native OGC

  • Posted on: 1 December 2021
Contributing Author Information:
Contributed by: 
Chris Holmes, OGC Visiting Fellow

Towards a cloud native OGCChris Holmes reflects on his time as OGC's first Visiting Fellow, and outlines his vision of 'Cloud-Native Geospatial' by addressing the question  ‘What would geospatial standards look like if they were built for the cloud?’

Chris has taken the time to look at the entire geospatial landscape and the potential for OGC to play the key leadership role in making the Cloud-Native Geospatial vision a reality. 

Vijay Krishnan from Intel Geospatial on AI, the Cloud, and the future of data integration, visualization, and analysis

OGC Member Intel recently launched Intel Geospatial, a cloud-based geospatial data management, visualization, and AI platform with applications in asset management across utilities smart cities, energy, and other industries. We sat down (virtually) with Intel Geospatial General Manager, Vijay Krishnan, to discuss AI, the Cloud, and what the future of data integration, visualization, and analysis may look like.

Big Processing of Geospatial Data

Geospatial Data has always been Big Data. Now Big Data Analytics for geospatial data is available to allow users to analyze massive volumes of geospatial data. Petabyte archives for remotely sensed geodata were being planned in the 1980s, and growth has met expectations. Add to this the ever increasing volume and reliability of real time sensor observations, the need for high performance, big data analytics for modeling and simulation of geospatially enabled content is greater than ever. In the