The section of Soil Physics and Hydropedology at the Department of Agroecology (AGRO) at Aarhus University carries out research in soil’s physical, chemical, and hydrological processes. The aim is to gain a quantitative insight into the spatial distribution of soil properties on a local, regional, and national scale. The section will contribute with an integrated research effort, which will result in detailed mapping of peat areas.

The Department of Agroecology is leading within mapping soil, just as well as digital mapping using artificial intelligence (AI) techniques is a top competence. The department holds a GIS team, which is responsible for field data and soil maps on a national level and, among other things, carried out the latest mapping of peat soils in Denmark.

In the ReDoCO2 project, the team is responsible for policy support in relation to all soil-related inquiries, along with the Soil Physics and Hydropedology.

I•GIS is a specialized GIS and geoscience software and service provider, with an international client base. They are deeply engaged in research and development, both nationally and internationally. Their observations on market, customers, competitors, and technology indicate the existence of a lucrative market to be exploited by the developments in ReDoCO2.

I•GIS will utilize their participation in this project to continue building state-of-the-art applications for 3D geological modelling founded in research and end-user workflows. This project allows their set of modelling and data management solutions to be expanded to include modelling and decision support tools in the special case of peat mapping. However, many of the new features to be developed are believed to be general and applicable in other use-cases, and will hopefully be positive assets to already existing and future customers.

SkyTEM is an airborne geophysical company with 50+ employees and worldwide operations. Over more than a decade, the company has applied its strategic competencies in order to identify business opportunities, develop novel technologies within its field of application and successfully introduce new commercial products within the market of airborne electromagnetic methods (EM).

In the context of this project, we have identified a new business opportunity, entailing that the company’s broad research and development competencies are leveraged to develop a high-resolution TEM sensor (SkyTEMPico) mountable on a UAV. SkyTEMs project exit strategy is to extend the business foundation with a new business unit that produces and sells combined SkyTEMPico system and service solutions.

Central Denmark Region (CDR) covers 19 municipalities. It is the authority of soil pollution and raw material planning and administration, while coordinating regional development within climate adaptation and mitigation, rural development, circular economy and sustainability. Central Denmark Region works primarily in partnerships with municipalities, utilities, knowledge institutions etc. to find innovative and sustainable solutions to societal challenges. Coast to Coast Climate Challenge (C2C CC) is an example of a climate adaptation project where Central Denmark Region, as the secretariat of the project, collaborates with 31 partners to build a resilient region in response to the changes in the climate. The region promotes added value and multifunctionality as part of considering all interests and challenges in a project area.

Central Denmark Region has initiated a complementary project on Multifunctional Land Consolidation including a decision support tool, as the solutions to these challenges are best addressed in cooperation. This is especially the case with regards to converting agricultural land into biodiverse ecosystems which are currently under pressure. Specifically, Central Denmark Region contributes to ReDoCO2 by securing the applicability of the data and making sure that the project meets the needs of end users.

The Visual Analysis and Perception Laboratory at Aalborg University has conducted research within computer vision and machine learning for more than 20 years. The lab contributes scientifically and practically to the project within all aspects related to data integration and machine learning. The lab expects to advance their general competences of applying data science in relation to soil science, and in particular applying deep learning algorithms to diverse data types.