VALENS: Persistent Anchorage Monitoring
Through using ISR planes, optical, RF and SAR satellite imagery, we achieve persistent anchorage monitoring regardless of cooperability from individual ships.
AI4Copernicus is a European H2020 project that aims to bridge Artificial Intelligence (AI) with Earth Observation (EO) world by making the already developed AI4EU AI-on-demand platform, the digital environment of choice for users of Copernicus data, for researchers and innovators.
AI4Copernicus aims to bridge Artificial Intelligence (AI) with Earth Observation (EO) world by making the AI4EU AI-on-demand platform the platform of choice for users of Copernicus data along the value chain (scientists, SMEs, non-tech sector).
AI4Copernicus will achieve this by exposing AI4EU resources on EO data (DIAS – data and information access services) platforms, making it easy to procure computing power and large EO data, as well as to access training material and expertise.
AI4Copernicus proposes to reinforce and optimise the AI4EU platform service offering with AI4Copernicus datasets, tools and services relevant to Copernicus data to facilitate the use and uptake of the platform resources in domains of high economic and societal impact, such as in Agriculture, Health, Energy and Security.
A series of 4 open calls will be implemented, leading to 8 small-scale experiments (smaller, single-beneficiary experimental projects targeting technology-advanced users) and 9 use-cases (larger-budget projects, involving at least one non-technology user). The open calls will necessitate the utilisation of DIAS platforms, Copernicus data, the AI4EU platform and the services and resources that will be provided by the AI4Copernicus project.
Through organising, facilitating and mentoring these Open Calls, AI4Copernicus will reach out to new user domains and boost the use of the AI4EU platform. More specifically, AI4Copernicus aims to:
Through using ISR planes, optical, RF and SAR satellite imagery, we achieve persistent anchorage monitoring regardless of cooperability from individual ships.
Humanitywatch is aimed at assistance actors (humanitarian and development), and institutional actors, all over the world thanks to AI and Earth Observation technologies.
Through the integration of the Earth Observation data in the usage of AI based applications we could give an insight into what is happening with the environment in fragile and hard to reach areas. The areas of interest here are Ukraine and Mali.
We improve short term solar irradiation forecasts in the 15 minutes to 2 hours range, which is critical for off grid sites or isolated power grids, using AI and DL techniques.
Automatic detection of in-field weeds using super-resolved Sentinel-2 at 1m per pixel imagery and delineated field boundaries.
We broaden the interpretation of intermediate values of NDVI corresponding to the growing stage of a crop with the aim of helping farmers to assess the growth evolution. NVDI extracted from the imagery from the satellite Sentinel-2 for 4 years, for 11 plo...
EO4NOWCAST is a containerized application that comprises: - a machine learning pipeline for the hourly prediction of the soild moisture based on the NDMI maps from Copernicus S2 and ground based precipitation; - a machine learning pipeline for the 5-min p...
EO4NOWCAST is a containerized application that comprises: - a machine learning pipeline for the hourly prediction of the soild moisture based on the NDMI maps from Copernicus S2 and ground based precipitation; - a machine learning pipeline for the 5-min p...
The containerised application contains TFS, a Bayesian Optimization of the state-of-the-art AI, N-Beats. The TFS is one of the components of ESFA's AI. TFS Docker Image has been adapted to work with any kind of time series, from weather to Bitcoin prices ...
Fertirec offers Nitrogen Recommendation services for four major crops (Wheat, Barley, Rapeseed, and Corn) at both the postcode and district levels. This service is available for customers in the European Union (EU) and the United Kingdom (UK).
The aim of this project is to develop an AI-based service that assists livestock farmers decision-making by leveraging IoT and satellite imagery. The service is focused on optimizing the use of the available natural resources and reducing the negative eff...
The Lobelia Air project focuses on calibrating low-cost air quality sensors using AI techniques leveraging on EO derived AQ and meteorological data.
Maritime transport plays a vital role in our global economy, but navigating the vast ocean can be challenging. The AI4Copernicus ODFuse4Ship project, led by our Ocean-tech start-up AMPHITRITE, is set to change that by harnessing the power of satellite dat...
OPTIMAL for cOPernicus irrigaTION mAnagement tooLkit is a combination of state-of-the-art Machine Learning techniques that is designed to forecast environmental parameters which enable the delivery of irrigation needs intelligence.
Climate change, with its accompanying global warming and increasingly severe weather conditions, presents substantial risks to agricultural sustainability worldwide. This is driving the agri-food sector to adapt through the use of new technologies and str...
The project integrates AI-based nowcasting of satellite data and digital twin models of telecommunication assets to produce a risk assessment map for wildfire risks based on the forecasting of environmental and ground quantities and the presence and relev...
AI4 E2O.GREEN is a pipeline for automated Golf and Grassland Monitoring through interactive maps and computer vision models based on Copernicus data and services.
UrbAlytics aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigat...