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ReDoCO2 - Reduction and Documentation of CO2 emissions from Peatlands

Depending on their present condition, peatlands can store a huge amount of carbon (C) or be a source of carbon dioxide (CO2), which is a crucial challenge globally. In Denmark, peatlands constitute a key target to achieve the national goal of reducing CO2 emissions with 70% by 2030. There is thus a great need to investigate the spatial variability of peat soil properties in order to assess the total amount of C stored in these soils.  

Through the combination of state of-the-art hardware, software, modelling techniques and IT technologies the present project will develop an overall methodology to map peatlands in detail and enable accurate estimates of CO2 emissions and potential C stocks. This methodology will provide decision-makers with detailed information and cost-effective tools to appropriately select which peatland areas to take out of agricultural production and restore. Notably, the combination of the drone-mounted cutting-edge geophysical sensors, advanced 2D modelling techniques and 3D software will be a game changer for peatland mapping both nationally and worldwide.  

The project started in the fall of 2020 and runs for four years. The total project budget is DKK 32.3 million where DKK 24.3 million is funded by the Innovation Fund Denmark. The project consortium covers representatives from the Department of Agroecology at Aarhus University, Aalborg University, Region Midtjylland, SkyTEM, and I∙GIS.

For more information visit the project homepage or contact us at  


GAP - Groundwater Architecture Project

In an ambitious two-year project, Stanford University partners with leading Danish companies and three water agencies in California to develop a template for an optimal workflow that would use airborne electromagnetic (AEM) data as the foundation for the development of hydrogeological conceptual models. This provides a key step in the implementation of the Sustainable Groundwater Management Act (SGMA) in California and will provide value to groundwater architecture mapping worldwide. The workflow includes not only the deployment of the AEM technology to acquire vast amounts of AEM data, but more importantly designing the supporting geophysical and computational infrastructure for data analysis, interpretation, and archiving. Significant advancements can be made by studying ways in which California can develop and implement a workflow and build on Danish experience.

The Project is partly financed by The Danish Eco-Innovation Program (MUDP) and partly by the state of California and is a 2 year project running from September 2018 including Stanford Univeristy, Aarhus University, Aqua Geo Framework, Rambøll, and I•GIS

For more information contact us at  


RESPROB - Probabilistic Geomodeling of Groundwater Resources

Probabilistic Inversion | WorkflowGroundwater mapping in Denmark is internationally acknowledged and regarded as a benchmark approach. Huge amounts of data (well logs, geophysical, geo- and hydro-logical data) have been collected. Today these data are combined in a deterministic sequential workflow, where, typically, a single final model represents all available information. While successful, this workflow has some limitations: There is no way to ensure the final model consistency with all information at hand, and there is no way to ensure correct uncertainty quantification. To remedy this, we propose to develop a probabilistic data integration workflow that allows consistent integration of well-log, geophysical and geological data. The results will be a probabilistic geological model that: a) will be consistent with all data, and b) allow detailed uncertainty analysis. The resulting probabilistic geo-model can be efficiently used by the end-users for informed, data-driven, decision- making and for risk assessment.

The Project is partly financed by The Independent Research Fund Denmark and is a 3 year project running from May 2018 including University of Copenhagen, Aarhus University, United States Geological Survey (USGS), University of Cagliari, and I•GIS

For more information contact us at  


MAGIC - Mapping Geology in Cities

Based on the increasing urbanization, building activity, contamination, and drainage of climate change induced heavy precipitation, a need for knowledge about the urban subsurface in modern cities is  grow

Urban Geology | Voxel model

ing fast. To obtain this knowledge, a massive amount of geotechnical data, and tools to obtain, visualize and interpret on these data are required. This project attack these challenges, and aim to develop integrated tools enabling non-geoscience experts to both generate and and utilize urban geothechnical information.

To achieve this, the MAGIC project aim at delivering:   

  • Geophysical Instruments adapted to the urban environment 
  • Virtually 100% automated data processing and inversion software
  • High-Resolution 3D geological modeling software incorporating city infrastructure and geophysical inversion results
  • Unified and seamless data workflow

The Project is partly financed by EUROSTARS  and is a 2 year project running from April 2016 including Lund Technical Univeristy, Guideline Geo AB, Aarhus University, and I•GIS

For more information contact us at  


GeoSmart Cities

This project intend to develop a foundation for geological modeling in cities. By developing tools and workflows for urban geological modeling the hope is to enable Detailed model | Voxel model | Vector Themesthe cities to utilize the geological potential with respect to more sustainability concerning water supply, climate adaption, and heat storage. To achieve this, the project has two main scientific goals: 

  1. Improve management, design, and construction of urban subsurface infrastructure through new methods of urban geological mapping by activating and including non-digitized geological archive data. 
  2. Enable High Definition 3D visualization allowing easier and better utilization of the geological information and the different geodata.  

The Project is partly financed by the Danish Eco-Innovation Program (MUDP) and is a 2 year project running from January 2016 including GEO, University of Copenhagen, Frederiksberg Forsyning and I•GIS

For more information contact us at  


ERGO - Effective High Resolution Geological Modelling

The goal of this project is to develop a user-friendly expert AEM data | Smart Interpretation | Machine Learning

system that can combine a vast amount of 
different geo-data with geological expert knowledge. One part of the system will utilize machine learning approaches developed through the research projects enabling fast and accurate geological interpretations. The other part will be based on the development of the theory of Multiple Point Statistics (MPS) to allow correctly integration of soft (uncertain) data in the MPS simulations.  
The project is partly financed by the Danish National 
Multiple Point Statistics | MPS | Probabilistic Modelling
Advanced Technology Foundation and is a 3-year project running from November 2013 including The Geological Survey of Denmark and Greenland, University of Copenhagen and I•GIS. 

You can follow the project on Research Gate, or contact us directly at