[Esip-machinelearning] OGC Testbed-14: Machine Learning Engineering Report

[Mcgibbney, Lewis J (398M)]

http://docs.opengeospatial.org/per/18-038r2.html


This OGC Engineering Report (ER) describes the application and use of OGC Web Services (OWS) for integrating Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) in the OGC Testbed-14 Modeling, Portrayal, and Quality of Service (MoPoQ) Thread. This report is intended to present a holistic approach on how to support and integrate emerging AI and ML tools using OWS, as well as publishing their input and outputs. This approach should seek efficiency and effectiveness of knowledge sharing.

This engineering report will describe: experiences, lessons learned, best practices for workflows, service interaction patterns, application schemas, and use of controlled vocabularies. It is expected that the description of workflows for geospatial feature extraction will be more complex than the implementations found in the deliverables.

Dr. Lewis John McGibbney Ph.D., B.Sc.
Data Scientist III
Computer Science for Data Intensive Applications Group (398M)
Instrument Software and Science Data Systems Section (398)
Jet Propulsion Laboratory
California Institute of Technology
4800 Oak Grove Drive
Pasadena, California 91109-8099
Mail Stop : 158-256C
Tel:  (+1) (818)-393-7402
Cell: (+1) (626)-487-3476
Fax:  (+1) (818)-393-1190
Email: lewis.j.mcgibbney at jpl.nasa.gov<mailto:lewis.j.mcgibbney at jpl.nasa.gov>
ORCID: orcid.org/0000-0003-2185-928X

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