Welcome to EO-PERSIST
A CLOUD-BASED REMOTE SENSING DATA SYSTEM FOR PROMOTING RESEARCH AND SOCIOECONOMIC STUDIES IN ARCTIC ENVIRONMENTS
ABOUT EO-PERSIST
EO-PERSIST is a 48-month research project
funded by EU and the HORIZON TMA MSCA Staff Exchanges call aimed at understanding the impacts of climate change on Arctic environments, infrastructures, and industries. To achieve this, we are developing a cloud-based system that will allow us to collect, manage, and analyze Earth Observation (EO) data that is suitable for permafrost studies.
By leveraging recent advances in EO sensors, cloud computing, geographical information systems (GIS), and socioeconomics, we are creating a unique opportunity to promote pioneering research and socio-economic studies in the Arctic.
Some of the key features and Benefits of EO-PERSIST
- A continuously updated ecosystem with EO datasets suitable for permafrost studies.
- Methodological advances in permafrost studies by exploiting the huge volume of EO datasets.
- Indicators directly connected with socioeconomic effects to permafrost dynamics.
- Showcase of our system through five carefully selected and innovative Use Cases that will serve as Key Performance Indicators.
- Collaboration between staff from academia and industry via a series of carefully-designed secondments, establishing a unique fertile collaborative research and innovation environment.
10
Partners
7
Countries
1 568 600 EUR
EU contribution
48
Months
1.
Our Vision
The EO-PERSIST project is a collaborative research and innovation initiative that aims to establish a fertile environment for staff exchanges, knowledge sharing, and know-how transfer.
2.
Our Objectives
Technologies
Cloud Computing
Data storage and processing to provide easy access to large amounts of data and to enable efficient processing of that data.
Earth Observation Sensors
EO-Persist will rely on open satellite EO data, which will be used to develop and validate methods and models included in the project.
Geographic Information Systems
Used to analyze and visualize the EO data and modeling the results, developing tools and models for decision-making in the Arctic land regions.
GNSS
Used to provide efficient, stable, and safe data connection to ensure secure, stable and fast access to various data repositories.
Data Fusion
Combine EO data, social-economics and social media data with other data sources to improve the accuracy of results.
Big Data
Used to store and manage the large amounts of EO data that will be collected by remote sensing technologies.
Use - Cases
UC 1: Assessing the impact of permafrost thawing on land degradation and ecosystems.
UC 2: Modeling climate downscaling for enlarging resolution in image data via deep learning techniques.
UC 3: Explore the use of new geospatial data analysis methods for mapping coastal areas changes and quantifying the socio-economic impact of climate change on the Arctic region.
UC 4: Exploit the phase information of distributed scatterers to improve the spatial continuity which can reveal and estimate ground deformation patterns in greater detail.
UC 5: Consolidate algorithms for retrieving soil Freezing/Thawing (F/T) and Snow Water Equivalent (SWE) from SAR data applicable to both L- and C-bands and develop methods for using physical snow models to support retrievals from active and passive microwave sensors.
Events
23
Event 1
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18
Event 2
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News
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