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J26: The ESA Digital Twin Earth (DTE) Break-out Session
1:30pm - 2:45pm
Session Chair: Giuseppe Ottavianelli
Location:J - Online Sessions
Forest Digital Twin Earth Precursor: towards a digital representation of the world's forests
VTT Technical Research Centre of Finland, Finland
Forest Digital Twin Earth Precursor (DTEP) is one of the six Earth Digital Twin Precursors initiated by the European Space Agency. Forest DTEP aims to develop a subsystem of a very high precision digital model of the Earth, a digital twin of the planet, running on cloud infrastructure. It is being developed by a consortium of VTT Technical Research Centre of Finland, Department of Forest Sciences of the University of Helsinki, Simosol OY, Unique GmbH, Cloudferro Sp z o.o., and Romanian National Institute of Forest Research (INCDS). Forest DTEP glues together Copernicus data, the PREBASSO forest ecosystem carbon model and climate scenarios using the SIMO forest simulation software and a flexible user interface. It enables estimation, prediction and simulation of forest carbon stocks and the direct climate forcing of forest at a very high spatial resolution for the existing environmental conditions as well as different future climate scenarios. The precessing is performed using the Forestry Thematic Exploitation Platform (F-TEP) running on CreoDIAS. Due to its open architecture, the framework is not limited to any specific models and information retrieval tools. In the precursor stage, the system was demonstrated with users from Finland, Germany and Romania.
DTE Hydrology: A Prototype Of DTE With Focus On Water Cycle And Hydrological Processes And Its Impacts
National Research Council of Italy, Italy
Both the European Union (EU) Green Deal and the new EU Data Strategy propose to bring together European scientific and industrial excellence to develop a very high precision digital model of the Earth, capitalising on an effective integration of the latest advances and increasing capabilities of Earth observation systems (satellites, in-situ data, citizen observations), high resolution Earth system modelling, Artificial Intelligence, Information and Communication Technology and High Performance Computing capacities. Specifically, there is the urgent need to develop a digital modelling platform to visualize, monitor and forecast natural and human activity on the planet in support of sustainable development and to predict and manage environmental disasters. This activity is particularly important in our times due to the major environmental and societal challenges that must be addressed.
The European Space Agency (ESA) DTE Hydrology project aims at fostering a fast step forward towards establishing a solid scientific and technical basis to realise a Digital Twin Earth focused on the water cycle, hydrology and their different applications.
The ESA Digital Twin Antarctica project
The University of Edinburgh
The Digital Twin Antarctica project aims at generating an advanced dynamic reconstruction of Antarctica’s hydrology, and its interaction with the ocean and atmosphere, to be used by stakeholders such as decision-makers, agencies, managers, scientists and engaged citizens. The objectives of the reconstructions are to combine state of the art observation of past and current state combined with simulation of past, present and future state of the Earth system in and around Antarctica.
Within this demonstrator, we plan to advance ice sheet products via use of advanced Machine Learning techniques, via combination of data and regional climate model simulations, provide simulations of system state out with the reach of observation, and simulate the system evolution under future scenarios. These products will be combined in a dynamic interactive reconstruction that will enable the user to navigate the various products, explore interactions between components, and efficiently access key summary information and figures. This demonstrator will also lead to a roadmap for a fully operational Digital Twin of Antarctica and its integration within the larger ecosystems of Digital Twins.
The ESA DTE Ocean project
The Rise of Artificial Intelligence for Earth Observation (AI4EO)
The digital revolution is accompanied by a sensing revolution that provides an unprecedented amount of data on the state of our planet and its changes. These new global data sets from space lead to a far more comprehensive picture of our planet. This picture is now even more refined via data from billions of smart and inter-connected sensors referred to as the Internet of Things. Such streams of dynamic data on our planet offer new possibilities for scientists to advance our understanding of how the ocean, atmosphere, land and cryosphere operate and interact as part on an integrated Earth System. It also represents new opportunities for entrepreneurs to turn big data into new types of information services. The application of AI to EO data is just at its infancy, remaining mainly concentrated on computer vision applications with Very High-Resolution satellite imagery, while there are many areas of Earth Science and big data mining / fusion, which could increasingly benefit from AI, leading to entire new types of value chain, scientific knowledge and innovative EO services. This session will present some of the ESA research / application activities and partnerships in the AI4EO field, inviting you to stimulate new ideas and collaboration to make the most of the big data and AI revolutions.
Enabling EO Platform Capabilities paving the way towards Digital Twin Earth
The realisation and implementation of Digital Twin Earth (DTE) will require alignment with, and in some cases, incorporation of existing European EO capabilities and assets. Some key EO platform capabilities initiated by ESA and developed in collaboration with European industry have direct relevance for the development of DTE. The presentation will highlight the ESA platform ecosystem approach and showcase EuroDataCube and openEO Platform as key resources that are demonstrating a step change in European EO platform assets and provide foundational capabilities for the implementation of DTE.