Imagine trying to solve a puzzle where the pieces come from different boxes and don't fit together. This project creates a universal translator for environmental data, making satellite images, ground sensors, and citizen reports speak the same language. It turns messy, scattered information into a clean, organized library that anyone can use to track pollution and nature loss.
Environmental data is currently trapped in silos, using different formats and scales, which makes it impossible for companies to get a reliable, single view of pollution or biodiversity impacts.
A prototype of the Green Deal Data Space (GDDS) and automated tools for creating semantically enriched data formats using OGC standards.
If you are a sustainability auditing firm dealing with fragmented data from different sources — this project developed a way to automatically link data to known vocabularies that ensures reports on air and water quality are reliable and standardized.
If you are a smart city technology provider dealing with incompatible IoT and satellite feeds — this project developed a prototype using the Eclipse Dataspace Connector that allows secure and interoperable exchange of environmental data.
If you are a precision farming software developer dealing with low-quality ground data — this project developed machine learning tools to ensure the trustworthiness of data and transform spatial scales for better accuracy.
Based on available project data, no specific pricing or cost structures are mentioned as the project focuses on creating an open hub for FAIR data.
Yes, the project specifically focuses on creating an interoperable and scalable manner of integrating data from satellites, IoT, and socio-economic sources.
Based on available project data, the results are exposed via standards from the International Data Space Association and the Eclipse Dataspace Connector, but specific licenses are not listed.
It uses the Sensor Things API (STA) for exporting in-situ data and the OGC API Coverage standard for multidimensional data cubes.
The project period runs from 2022-09-01 to 2025-08-31, with prototypes already deployed and connected.
The consortium is research-heavy with 7 research organizations and 1 university, but maintains a 23% industry ratio with 3 industrial partners, including 2 SMEs. This balance suggests the project is grounded in academic rigor while having a direct line to commercial application through the 8 countries involved.
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