Data Spaces are born in the big data paradigm where many sources produce constant streams of Earth observation data (remote sensing, citizen science, IoT sensors and in-situ). The traditional organization and computation of data is no longer efficient as data is constantly evolving and mixed in new ways, thus making more difficult to extract knowledge and even more in a standardized way.

The Green Deal Data Space (GDDS) is the solution provided by the EC to use the major potential of data in support of the Green Deal priority actions on climate change, circular economy, pollution, biodiversity, and deforestation. In this context, an increasing number of in-situ sensors producing observations of a reasonable quality are being deployed in several fields, i.e., air quality, water quality, animal camera trapping..., but also in the form of citizens collecting environmental data through mobile apps, online forms, etc. Many of these systems have wireless connections which allow to automatically make data available into central repositories (such as Research Infrastructures, Digital twins and Data lakes) that can then be part of a Data Space.

This session aims to explore new in-situ data solutions in the context of the GDDS to leverage data integration and data management in a more efficient way, particularly, but not limited to:

- systematized in-situ observation requirements gathering/formulation to address data needs and gaps,
- sensors deployment, collection and integration in servers following geospatial standards (i.e. STAplus, Camtrap DP, etc.),
- semantic uplifting for citizen science data cataloguing and searching,
- authentication issues for sensible data transferring,
- integration, computation and analysis of in-situ (sensors, citizen science, field campaigns) into and/with satellite-based data,
- sensor requesting, integration and visualization tools,
- in-situ data cataloguing initiatives,
- others.