For more information please contact innovation [at] ogc.org (subject: OGC%20Testbed-18%20Information)
Update 1 November 2022: Testbed-18 online outreach and demonstration days will be on January 17th and 18th, 2023.
Update 31 October 2022: November 18th, 2022, is the final deadline for posting the DER (Draft Engineering Report) to pending to meet the 3-week rule before the technical committee (TC) electronic vote for publication.
Update 15 July 2022: With 3D+ Data Standards and Streaming kick-off on 15 July 2022, all the tasks of Testbed-18 have already started, and participants are actively engaged in the necessary activities to produce the Engineering Reports.
Update 26 April 2022: We are in the final negotiations stage with bidders for the first four tasks that will kick-off May 3-5, 2022, in a virtual meeting. Please find the tentative schedule for this meeting below.
Update 22 April 2022: Thanks a lot for all your proposals! We have started negotiations with bidders and will get in contact with you soon. Negotiations for three tasks, 3D+ Data Standards and Streaming, Machine Learning Training Datasets, and Moving Features and Sensor Integration, will start a bit later in April as we wait for final sponsor contracts to come in.
Testbed-18: Advancing Location Interoperability
Version 2.0 / 22 July 2022
1. Help Boosting Location Interoperability
OGC Testbeds are OGC’s largest Innovation Program (IP) initiatives. Testbeds boost research and development to make location data and information more FAIR: Findable, Accessible, Interoperable, and Re-Usable. Testbeds provide a unique opportunity for sponsors to tackle location data and processing challenges together with the world’s leading geospatial IT experts.
As part of the Testbed development process, challenges put forth by sponsoring organizations are refined and mapped to a set of work items that OGC member organizations will compete to address. The Innovation Program team, together with the Testbed sponsors, will then select the most qualified organizations to join Testbed-18. In a collaborative effort, all Testbed participants, sponsors, and the OGC team work jointly on the goal to stepwise increase Technology Readiness Levels (TRL) of geospatial IT solutions, including software architecture, interface design, information and data models, as well as any related standards and specifications.
2. Synergistic Effects
OGC Testbeds provide a unique opportunity to explore technologies that appear disconnected at first glance. Combining these technologies with a single initiative and bringing several sponsors together allows us to create an interoperability environment that comes much closer to real-world situations. Consequently, Testbeds allow leveraging outstanding quality of synergetic effects to address challenges that require collaboration among several sponsors and experts from member organizations.
As multi-sponsor initiatives, Testbeds benefit from synergistic effects caused by overlapping interests. Sponsorship for individual tasks can be shared across sponsors, which enables the Testbed to explore new technology more deeply and ensures more realistic use cases and scenarios. The IP team collects the sponsors' areas of interest early in the process and shares the full picture among all sponsors. This allows sponsors to identify common interests and leads to more efficient use of the available resources.
Testbed-18 addresses seven main topics (Tasks) categorized under three threads, as illustrated in the figure below:
3. Thread Summaries
Thread 1: Advanced Models and Data (AMD)
For the first time, we look beyond earth and explore the sensor and object integration of assets in celestial orbits or in free flight in our solar system. With the growing commercialization of space, exact tracking and localization of data will become more and more important. This task develops the necessary reference system interoperability.The participants and their roles in this task are presented in the figure below:
Machine learning training datasets will help us understand how to standardize ML training data in order to guarantee the high quality of future ML models and compatibility. The Engineering Report name and its participants are presented in the following figure.