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Stay tuned! One of these finalists will become the ESA Digital Twin Earth Challenge winner!
DeepSentinel combines the merits of Sentinel-1 and Sentinel-2 imagery in a general-purpose computer vision model. This addresses two perennial problems in Earth observation and machine learning: how to handle cloud and atmosphere interference, and how to overcome a shortage of training labels. Merging Sentinel-2 optical imagery and Sentinel-1 synthetic-aperature radar imagery provides insights that can penetrate clouds, while the self-supervised training curriculum makes use of the massive amount of available Copernicus data. The goal is to unleash an explosion of use cases for Sentinel-1 and Sentinel-2 imagery in the same way that pre-trained computer vision models did for computer vision applications of conventional imagery five years ago.
förecast: The New Generation of Forest Intelligence
förecast is a deep-tech forest intelligence solution that is designed to offer accurate and up-to-date assessments of forest resources on a multi-purpose platform. It provides digital twins of forests by leveraging and integrating real-time data from the Copernicus satellite Sentinel-2, airborne LiDAR, aerial orthoimages, and ground plot sampling to create advanced algorithms that offer unparalleled accuracy, reliability, and high resolution to forest assets across different ecosystem services. The system supports decision-making with regard to vegetation structure, volume, biomass or CO2 stocks, and growth and terrain characteristics. This allows forest users to create reliable and real-time inventories of specific forest areas through rapid, cost-efficient operational mapping that delivers advanced decision support with artificial intelligence.
Its ability to combine open data from Copernicus, LiDAR, artificial intelligence, and benefits to society, the economy, and the environment puts förecast at the competitive forefront in terms of how geospatial and remote-sensing technologies can be harnessed to optimise sustainability, generate value, and create great business opportunities.
Urban Green from Earth Observation (U-GEO)
U-GEO is an algorithm based on machine learning that combines Copernicus data with urban planning, resource management, and green infrastructure. It is designed to help create efficient green infrastructure and continuously monitor vegetation health conditions and performance.
The Copernicus data is used to correlate current vegetation planting in different urban environments with key performance parameters related to air pollution reduction, heat island minimisation, and biodiversity preservation.
The U-GEO algorithm is then employed to determine the optimal distribution of green infrastructure and maximise the key performance parameters while implementing urban planning and an economical budget as boundaries.
The second part of the system is based upon the continuous monitoring of green infrastructure through the daily information gathered by Copernicus satellites. This enables targeted on-site maintenance should the health conditions of urban vegetation or the efficiency of green infrastructure decrease.
The ESA Digital Twin Earth Challenge (ESA DTE Challenge) aims to create an interactive model of the Earth, which arises from the integration of individual models that accurately reflect diverse physical aspects of our planet.
Thus, ever more reliable information about past, present and future changes in the Earth system can be obtained. With this information numerous societal challenges can be addressed.
The underlying models are based on measurements and observations, while being complemented with state-of-the-art analytical techniques such as AI.
Submissions are welcome addressing key societal challenges, including:
The ESA DTE Challenge aims to increase the exposure and understanding of EO data combined with AI (Artificial Intelligence), IoT (Internet of Things) and Machine Learning, Cloud Computing and Data Analytics. Those tasked with evaluating the submissions to this challenge will therefore focus on the potential for technological feasibility, rather than its maturity.
Submissions to ESA Copernicus Digital Twin Earth Challenge will be assessed against the following criteria:
How innovative is the idea and its proposed expansion within the market?
Does Copernicus data significantly contribute to the application solution or does it also work without?
Does the technical implementation of Earth observation data allow for future scalability that will answer business needs?
Commercial Viability Index
Does the solution have real market potential? How many users you may reach?
How significant is the participant’s problem for society / the environment / business / EU policy?
The European Space Agency (ESA) is Europe’s gateway to space. Its mission is to shape the development of Europe’s space capability and ensure that investment in space continues to deliver benefits to the citizens of Europe and the world.
To contribute to the success of Copernicus, ESA is exploiting its 35 years of expertise in space programme development and management. While the Copernicus programme is politically led by the European Union (EU), ESA is the overall coordinator of the Copernicus Space Component and will, inter alia, ensure the uninterrupted delivery of data from the Copernicus Sentinel satellites and from an important number of Copernicus Contributing Missions at national, European and international level. Following the launch of Sentinel-1A on 4 April 2014 the Copernicus programme has entered its operational phase, serving users with an ever-increasing mix of satellite imagery and other data.
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