Publication Date: 2020-10-26
Approval Date: 2020-09-23
Submission Date: 2020-08-27
Reference number of this document: OGC 20-045
Reference URL for this document: http://www.opengis.net/doc/PER/EOAppsPilot-CRIM
Category: OGC Public Engineering Report
Editor: Tom Landry
Title: OGC Earth Observation Applications Pilot: CRIM Engineering Report
Copyright © 2020 Open Geospatial Consortium. To obtain additional rights of use, visit http://www.opengeospatial.org/
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- 1. Subject
- 2. Executive Summary
- 3. References
- 4. Terms and Definitions
- 5. Overview
- 6. Earth Observation Platform
- 6.1. Overview
- 6.2. EO Platforms Deployment
- 6.3. Climate Platforms
- 6.4. Interoperability Experiments
- 6.5. Applications and Processes
- 6.6. Platform Architecture
- 7. Remote Sensing Application
- 8. Machine Learning Application
- 9. Climate Services
- 10. Earth Observation Applications
- 11. Conclusion
- Appendix A: Climate Indices Available Through Finch
- Appendix B: Process Description of Finch
- Appendix C: Remote Sensing Processing Graph
- Appendix D: JSON File for the Climate Process Execute Request Body
- Appendix E: CWL File for WPS 1.0 Provider
- Appendix F: JSON Request Body to Execute WorkflowSubsetPicker
- Appendix G: Technical Interoperability Experiments
- Appendix H: Example of Configurations for S3 Buckets Support
- Appendix I: Revision History
- Appendix J: Bibliography
This engineering report documents experiments conducted by CRIM in OGC’s Earth Observation Applications Pilot project, sponsored by the European Space Agency (ESA) and Natural Resources Canada (NRCan), with support from Telespazio VEGA UK. Remote sensing, machine learning and climate informatics applications were reused, adapted and matured in a common architecture. These applications were deployed in a number of interoperable data and processing platforms hosted in three Canadian provinces, in Europe and in the United States.
CRIM’s key findings are as follows:
From the application developer’s perspective, it was easier to first develop and test locally, and then use an OGC Application Programming Interface (API) to deploy and execute remotely. This is partly due to a smoother learning curve from application to packaging, than from package to platform.
Conformance classes for an Application Deployment and Execution Service (ADES) API should be standardized. An offset in the support of API elements subsists in participants' implementations.
Application packaging, deployment and execution are appropriately defined by combination of Common Workflow Language (CWL) and Docker images.
More tests are required to better support application workflows running in multiple platforms. In order to build a federated cloud, these tests have to run regularly, in a structured and systemic fashion.
Participants' findings should offer feedback into EOEPCA, for example for platforms intending to support machine learning (ML) services. From the application developer’s perspective, ML apps are well defined and should be deployable like any other app. It is not clear with the current EOEPCA use cases that the platforms will easily integrate ML services (annotations, trained models, access to GPU clusters, etc.).
The business value of this initiative is articulated in CRIM’s promotional videos, expressed in both the perspective of platform managers and application developers. The figure below presents the key value propositions of multidisciplinary workflows and federated infrastructures.