[esip-semantictech] OGC Testbed-14: Machine Learning Engineering Report - opportunities for collaboration

[Mcgibbney, Lewis J (398M)]

Hi Folks,
I have recently been cross posting some information in a bid to excite people and encourage some activity for the upcoming GeoSemantics Symposium which we will be co-locating with the ESIP Summer meeting.
The recent OGC Testbed-14: Machine Learning Engineering Report provides some very insightful intersections for us to gorge on, specifically semantic enablement of ML [0], deep learning semantic segmentation [1][2] and semantic interoperability [3]. The final topic represents open discussion and is ripe for us to address

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The Controlled Vocabulary Manager (CVM) provides a service for finding terms by various parameters. The service is used by the Machine Learning Clients to provide a list of terms for classifying features in an image. The CVM could be fully integrated into the OGC AI/ML architecture and used to provide a common vocabulary to all components within the architecture: ML System (WPS), Knowledge Base, Machine Learning / Image Analyst Clients, and Image/Feature Store. Other topics for consideration include standardization, from an OGC context, of the service interface and data exchange formats used by the CVM service, for example potential use of the OGC Catalogue Service interface (CSW) and data encodings such as GML.

Inline with Testbed-14 EOC Execution Management System (EMS) best practices, consider exploration of advanced platform-based use cases for AI. GeoInt literature describes "query interpreters" and subsequent "query conductors" for federated subsystems. Query interpreters use NLP, a form of AI, to understand text-based user queries and automatically produce federated workflows. Currently, work in EOC describes use of graphical workflow editors. Future work for EMS could include experiments on automatic generation of workflows or selection of catalogued workflows that are closest to a user query, thus advancing "query interpreter" concept. Experiments in the creation of Analysis Ready Data and Dynamic Analysis Ready Data could support such efforts.
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[0] http://docs.opengeospatial.org/per/18-038r2.html#_d166_semantic_enablement_of_ml
[1] http://docs.opengeospatial.org/per/18-038r2.html#_dl_semantic_segmentation
[2] http://docs.opengeospatial.org/per/18-038r2.html#_dl_semantic_segmentation_2
[3] http://docs.opengeospatial.org/per/18-038r2.html#_semantic_interoperability


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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 Dare Mighty Things
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