The evolution of planet Earth is everyone’s business

CRIM helps standardize the sharing of Earth data to make it accessible to all.

There are thousands of Earth observation data sources. Through expert analysis, we can help anticipate or even prevent natural disasters such as floods and forest fires. However, the state of the data (scattered, disorganized) is such that environmental forecasting becomes challenging.

That is why the CRIM has been working for many years with prestigious organizations such as Natural Resources Canada, Environment Canada, NASA and the European Space Agency to standardize and normalize Earth data. Together, we developed powerful applications that can: 

  • Analyze large volumes of heterogeneous Earth data using artificial intelligence,
  • Render them interoperable on next-generation platforms, 
  • Make them available in internationally recognized formats. 

As part of the Open Geospatial Consortium (OGC) framework, these efforts convey clear benefits: they facilitate the flow and sharing of data between people and computers, and give developers the ability to create compatible applications and services.

What is OGC?  

OGC is an international consortium of more than 500 companies, public agencies, research organizations and universities. Its mission is to make geospatial information and services available according to FAIR (Findable-Accessible-Interoperable-Reusable) principles. Members are called upon to develop pilot projects according to the OGC’s innovation program and then make them available to the public.  

The CRIM Geospatial Expert Group is currently working on its 7th innovation project with the OGC: “Applications-to-the Data Architecture”. Its instigator and main contact, Tom Landry, presented the results of the pilot on September 8, 2020 during a webinar organised by the OGC and attended by a number of major players. 


There is already the potential for significant outcome:  

  • With the Remote Sensing application, it becomes possible to measure at a higher rate the extent of a flood or forest fire,
  • With Machine Learning, we can recognize or even identify highly -sensitive areas (e.g. swamps and forests) and provide monitoring areas. 

We are extremely proud of the achievements propelled by this world class consortium.

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