Earth Observation Data in Environmental Data Spaces
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 5132
Special Issue Editors
Interests: remote sensing; land cover; sustainable development; citizen science
Special Issues, Collections and Topics in MDPI journals
Interests: data; metadata; web semantics; remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: data quality; citizen science; metadata
Special Issue Information
Dear Colleagues,
A data space is defined as an infrastructure that enables data transactions between different data ecosystem parties on the basis of a governance framework. Data spaces deploy data-sharing tools and services for processing data in an interoperable way. They include a data governance structure that is compatible with relevant legislation and that stimulates the application of FAIR principles. Underlying data spaces is trust, which is gained by having data quality and provenance documentation, authentication and authorization services, and clear license schemas enabling the reuse of data. Although the concept has become popular, its implementation in the Earth observation domain is still in its infancy.
This Special Issue focuses on the design principles of data spaces for Earth observation data as well as the standards, practices, and implementations that enable better data sharing of remote sensing and in situ environmental observations. In Europe, the Green Deal Data Space and the Agriculture Data Space are two examples of data spaces promoted by the European Commission. This Special Issue is also seeking to publish discussions on other emerging cyberinfrastructures that share a similar aim. Potential topics include, but are not limited to, the following:
- Data spaces for remote sensing data distribution and processing;
- Evolving spatial data infrastructures into data spaces;
- Architecture and components constituting a data space;
- Standards for developing data spaces;
- Data processing facilities and protocols;
- Tools and web services useful in data spaces;
- Integrating heterogeneous data sources into data spaces;
- Semantics enabling the integration of data;
- Artificial intelligence and machine learning in data spaces;
- Relation between data spaces and digital twins;
- Catalogues for data spaces;
- STAC and COG to enable remote sensing data spaces;
- Authentication, authorization, and transactions in data spaces.
Dr. Joan Masó
Dr. Alaitz Zabala Torres
Dr. Lucy Bastin
Dr. Kaori Otsu
Guest Editors
Manuscript Submission Information
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Keywords
- data quality
- provenance
- governance
- processing
- data space
- semantics
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