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Tag: AI4EO

SPATIAL – Soybean Price forecAsting based on saTellite-derIved services and Artificial intelligence

Prime company: HYPERTECH S.A. (GR)

The main objective of SPATIAL is to provide a proof-of-concept (PoC) prototype for forecasting soybean futures contracts price moves using Artificial Intelligence models based on financial & macroeconomic features and Earth Observation products. SPATIAL is realizing two distinct Machine Learning (ML) models, one for soybean crop yield forecasting and one for prediction of soybeans futures …

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AI4DROUGHT

Prime company: Lobelia Earth, S.L. (ES)

AI4DROUGHT is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster.  The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO and …

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DEEP EXTREMES

Prime company: Leipzig University (DE)

DEEP EXTREMES is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster.  The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO and AI to address a major …

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extrAIM

Prime company: National Technical University of Athens (GR)

extrAIM (AI-enhanced uncertainty quantification of satellite-derived hydroclimatic extremes) is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster.  The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO …

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IMITATE – Introducing Machine-learning Into Targeted Analysis for Terrestrial Ecosystems

Prime company: UNIVERSITY OF LEICESTER (GB)

The proposed IMITATE project aims to address the following questions: How well can machine learning methods emulate physical process-based land surface models, focused over Europe? Can explainable AI techniques provide new insights into process understanding when combining land surface models and Earth Observation data Are the learnt relationships between the modeled inputs and outputs consistent …

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Earth Observation Info Days 2022

March 9, 2022 - March 10, 2022

Introduction and Objectives The European Space Agency, ESA, is organising the EO Info Days to introduce activities in FutureEO-1 Block 4 “EO Science for Society”, Global Development Assistance (GDA), and Climate Change Initiative Opportunities (CCI+)  programmes. The main goals of these Info Days will be to present the upcoming opportunities for European entities to participate …

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EO Industry

Artificial intelligence for Earth observation: monitoring of wildfires

Wildfires are a natural component of the Earth system, important for nutrient release and vegetation growth. Climate change, however, is contributing to more frequent, more destructive and less predictable wildfires worldwide. Australia, in particular, experiences regular bush fires during the summer, but the devastating 2019-2020 bushfire season, known as the Black Summer, was unprecedented in …

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ADVANCED AI4EO FOR WILDFIRE MONITORING

Prime company: CGI IT UK LIMITED (GB)

The Artificial Intelligence for Earth Observation (AI4EO) Wildfires project began in December 2020 and run for 8 months, focussing on developing and demonstrating a burned area (BA) mapping service that combines EO data, specifically Sentinel-2 optical data, with an AI-enabled algorithm. The project consortium is led by CGI UK, utilising their legacy of developing cloud-based …

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Workshops

AI for Space and Earth Observation side event @phi-week

Learning AI is one of the fastest growing technology fields. Organisations are already starting to implement modifications to their business models due to AI and Machine Learning being widely adopted as a helpful tool to build solutions for resilient and sustainable societies that can adapt to the effects of climate change. Learning AI has thus …

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DeepSent – Deep Learning-Based Multiple-Image Super-Resolution for Sentinel-2 Data

Prime company: KP Labs Sp. z o.o. (PL)

The aim of the activity is to apply super-resolution reconstruction multispectral Sentinel-2 images, using multiple images of the same region, captured at different points in time (MISR, multiple-image super-resolution). This is achieved by adapting recent deep neural networks that were recently proposed for dealing with MISR, to the particularities of Sentinel-2 data. In particular the …

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