CEOS Analysis Ready Data Survey
This is your opportunity to help shape the future of CEOS-ARD!

With this survey we wish to better understand what the community values in the current version of CEOS-ARD as well as the priorities for future development.

We seek community feedback on matters that could be considered by an ambitious next-generation future CEOS-ARD strategy. In identifying matters for consideration, contributors may wish to reflect on evolving user needs, developments in space/ground/digital technology, and the changing sector landscape.

Many thanks for your time!
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Name (optional)
Organisation (optional)
Email address (optional)
Do you primarily work in the commercial, academic, or public sector? *
What most closely describes your role: *
To what extent do you agree that the following characteristics are needed to make an EO dataset 'Analysis Ready'?
Strongly Agree
Agree
Neutral / Unsure
Disagree
Strongly Disagree
Machine-readability
Findable, Accessible, Interoperable, Reusable (FAIR) compliance
Fitness for purpose indicators (i.e., that the product is suitable for a specific application)
Cloud-native formats
GDAL-readable formats
Consistent metadata specifications
STAC
AI/ML readiness
Interoperability through time (relative geolocation)
Consistent gridding/sampling frames
Interoperability with other datasets
Interoperability with in-situ data
Data quality ratings
Measurement uncertainties and traceability
Consistency (data has temporal and spatial consistency)
Data resilience (flexible and robust to changes in data sources and systems)
Pre-processed to a geophysical variable
Geometric correction using DEM (ortho-rectified)
Radiometric Terrain Correction (for SAR)
Cloud-free or cloud-masked
Durability (data source redundancy)
Accessibility
Consistent terminology
Clear selection
Any other characteristics not listed above which you believe are needed for a product to be 'Analysis Ready'?
If you’ve engaged with CEOS-ARD, what do you value most in the framework and what should be prioritised for future development?
For your purposes, do you see a need for formal EO ARD standards (e.g., through bodies like OGC, ISO, IEEE) or is a ‘community standard’ approach like CEOS-ARD sufficient? *
To what extent do you agree that the following characteristics are needed to make an EO dataset 'AI/ML ready'?
Strongly Agree
Agree
Neutral / Unsure
Disagree
Strongly Disagree
Temporal consistency
Standardised acquisition parameters
Minimal spectral variability
Pre-flight calibration
High-quality data and continuous QA/QC
Automated pre-processing
Supply of training datasets that are consistently and accurately labelled
Machine-readable formats
Easy access via APIs or cloud providers
Rich, consistent, useful and interpretable metadata
Large-scale accessibility
Rigorous definition of data values/content (no ambiguities)
Clear selection
Any other characteristics not listed above which you believe are needed for a product to be 'AI/ML Ready'?
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