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OGC Testbed 19 Analysis Ready Data Engineering Report
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| Definitionskos:definition | Implementations of the Analysis Ready Data (ARD) concept are consistent with the FAIR principles of finding, accessing, interoperating, and reusing physical, social, and applied science data with ease. The goal of this Testbed 19 OGC Engineering Report (ER) is to advance the provision of geospatial information by creating, developing, identifying, and implementing ARD definitions and capabilities. Specifically, this ER aims to increase the ease of use of ARD through improved backend standardization and varied application scenarios. Additionally, this work seeks to inform ARD implementers and users about standards and workflows to enhance the capabilities and operations of ARD. Ultimately, the goal of the work described in this ER is to maximize ARD capabilities and operations and contribute to the enhancement of geospatial information provision. Four distinct scenarios – gentrification, synthetic data, coverage analysis, and coastal studies – are explored to reveal both the strengths and limitations of the current ARD framework. The gentrification scenario, which utilizes existing Committee on Earth Observation Satellites (CEOS) ARD data, highlights the need to expand ARD’s scope beyond Earth Observation (EO) data. The integration of diverse data types, such as building footprints and socio-economic statistics, is crucial for comprehensive analysis. The synthetic data scenario explores the potential of simulated EO imagery to enhance data availability and diversity for machine learning applications. However, challenges in standardization and quality assessment require further investigation. The analysis of coverages for ARD reveals the importance of clear pixel interpretation (“pixel-is-point” vs. “pixel-is-area”) and standardized units of measure for seamless integration and analysis. Additionally, enriching the metadata structure with defined extensions is crucial for efficient data discovery and understanding. The coastal study scenario, where in-situ data needs to be elevated to ARD, emphasizes the need for flexible levels of readiness. Different analytical tasks may require distinct data properties, necessitating adaptable standards that cater to temporal emphasis, spatial alignment, and non-GIS applications like machine learning. This work identified several key areas for improvement: encompassing non-EO data such as building footprints, socio-economic statistics, synthetic data, and in-situ measurements; establishing guidelines and quality controls for incorporating diverse data types; tailoring data specifications to accommodate different analytical needs, including temporal emphasis and non-GIS applications; and implementing structured metadata with defined extensions for enhanced data discovery, understanding, and provenance tracking. In addition to the above recommendations, the interoperability and support of ARD in wider communities warrants further exploration and implementation. Additionally, areas such as uniform evaluation and compliance certification could be further investigated to ensure consistency in data readiness across various hierarchies and application domains. |
|---|---|
| Broaderbroader | Public Engineering Report |
| http://purl.org/dc/terms/createdcreated | 2024-07-05 |
| Creatorcreator | Liping Di, David J. Meyer,r Eugene Yu |
| seeAlsoseeAlso | https://docs.ogc.org/per/23-043.html |
| Statusstatus | valid |
| Notationnotation | 23-043 |
| Alternative LabelaltLabel | 23-043 |
| OGC Testbed 19 Analysis Ready Data Engineering Report | |
| OGC document typedoctype | Public Engineering Report |