1. Satellite Interoperability Workshop LSI/SDCG/GEOGLAM Joint Meeting
Committee on Earth Observation Satellites
Satellite Interoperability Workshop LSI/SDCG/GEOGLAM Joint Meeting
Steven Hosford, Jeff Masek
6th September, 2018

2. Context 1st Satellite Interoperability Workshop
13th – 15th August, Menlo Park
Convened by non-profit Radiant Earth
2 principal sponsors: Planet and Maxar
Close to 100 participants including private sector, non-profit sector, institutional
Majority of participants from US, but significant European presence also
Common Welcome session, then two parallel tracks
Analysis Ready Data
STAC
CEO presented CEOS Analysis Ready Data for Land

3. Context
Stated objective of ARD track: establish standards that will allow everyone to produce interoperable ARD
Primary workshop achievement: put everyone on the same page with respect to the what has been done already and what is out there in the “ARD space”
CEO presented CEOS Analysis Ready Data for Land on behalf of LSI-VC
Initial response: CARD4L framework relevant, but not at the core of workshop objectives
Workshop provided a forum to share information on what organisations were doing on “ARD”
Detail on
Issues preventing interoperability
Atmospheric corrections
Geometric corrections
Reference datasets (grids, DEMs, etc)
CARD4L framework is “high level”

4. Not an exhaustive list ...

5. Takeaways/talking points
Substantial interest and energy being put into simple, interoperable satellite data by the private/nonprofit sector.
Different motivations, but broad interest
CalVal/Data quality collaboration with a bootstrapping approach.
Ensure the sector benefits from 40 years of CalVal experience in SAs
Specific Cal/Val and harmonisation (RadCalNet, ASIXS, etc.) exercises between institutional and private constellation operators.
Need to ensure relevance of CalVal and ARD approaches for small/nanosat community
Role of institutional actors.
Growing commercial land surface imaging sector good for SAs
ARD is a game-changer for application of large scale Machine Learning approaches to satellite imagery.