AD4GD’s mission is to co-create and shape the European Green Deal Data Space as an open hub for FAIR data and standards-based services that support the key priorities of pollution, biodiversity and climate change.
| Component | Description |
|---|---|
| Pilot 1: Lakes | A scenario analysis tool for small Berlin lakes |
| Pilot 2: Biodiversity | Determining whether animal habitats are being disconnected |
| •Pilot 2: Data4Land | Enriching land-use/land-cover data with OSM and protected areas |
| •Pilot 2: Graphab | Containerised Graphab software for postprocessing |
| •Pilot 2: Biodiversity EBVs | Pilot 2 Environmental BioDiversity Variables |
| Pilot 3: Air Quality | Using IoT data to improve air quality forecasting |
| OpenEO Harvesting | Computing Remote Sensing Indices using OpenEO |
| Air Quality | IoT Sensor Ingestion Tools |
| AD4GD Connector | IDSA compliant Dataspace Connector desigend for the AD4GD Dataspace |
| STA+ | Sensor Things API |
| TAPIS | Tables from OGC APIs - A frontend tool |
| Data Cubes | OGC Coverages for Open Data Cube |
| GDIM | Green Data Information Model |
| Data harmonisation pipelines | Data harmonisation pipelines to generate GDIM compliant data |
| STA generation service | SensorThings API generation service from GDIM compliant data |
| Geonetwork Catalog | Catalog solution for geospatial data. |
| Data Trustworthiness | A framework for documenting the lineage, uncertainty and quality of datasets. |
| GDDS Terms | Green Deal DataSpace Vacabularies and Ontologies |
| GDDS Interfaces |
The focus will be on interoperability concepts that bridge the semantic and technology gaps which currently prevent stakeholders and application domains from multi-disciplinary and multi-scale access to data, and which impede the exploitation of processing services, and processing platforms at different levels including Cloud, HPC and edge computing. This project will enable the combination and integration of data from remote sensing, established Virtual Research Environments and Research Infrastructures, Internet of Things (IoT), socio-economic data, INSPIRE and Citizen Science (CitSci) in an interoperable, scalable and reliable manner. This will facilitate integration by including semantic mappings to different standards and dominant models bridging domain- and data source-specific semantic concepts such as the Essential Variables framework, as well as applying machine learning and geospatial user feedback to ensure quality, reliability and trustworthiness of data and transforming spatial scales.