Meet the winners of the 3rd Open Call for Experiments, with beneficiaries SMEs, startups, spin-offs (single partner projects) and a total funding up to €80.000 for each prototype (8 funded prototypes).

ODFuse4Ship – Ocean Data Fusion for Ship Routing

Company: AMPHITRITE SAS
Country: France
Domain: Maritime

Abstract: An ocean of Earth Observation data is available today through the Copernicus programme. Each one of the individual observation data or numerical model outputs has their own disadvantages, offering limited reliability for operational use. The fusion of satellite observations of the ocean from different sensors (infrared, visible, altimetry, radar) through advanced AI-Computer Vision methods can provide end-users with real-time, highly-reliable and high-resolution surface currents.

Lobelia Air – Machine Learning-powered Air quality monitoring and forecasting at your doorstep

Company: Lobelia Earth
Countries: Spain
Domain: Health

Abstract: Lobelia Air is an operational service developed to monitor and forecast air pollution at the hyper-local level. This proposal focuses on improving the current monitoring and forecasting results of the Lobelia Air system through machine learning-based integration of heterogeneous data sources including official monitoring stations, low/mid-cost sensors and atmospheric models.

EO4NOWCAST – Earth Observation for Severe Weather Hazard Nowcasting

Company: Artys S.r.l.
Country: Italy
Domain: Safety/Disaster Risk Reduction

Abstract: EO4NOWCAST’s ambition is to realise and demonstrate an operational and replicable approach to assess severe weather events and related hazards in the short term (nowcasting) built upon the synergy between EO and rainfall monitoring products. 

ESFA – Empirical Seasonal Forecasts for Agriculture

Company: Geoskop SL
Country: Spain
Domain: Agriculture

Abstract: Agricultural production has been increasingly exposed to unfavourable climate events and extremes in the last decades. These events can lead to heavy reductions in, and even failures of, crop yield quantity and quality, with potential regional-to-global consequences in the agricultural markets and trade patterns. Climate change is projected to further exacerbate this tendency. Copernicus C3S Seasonal Climate Systems, with their predicting time up to 6 months ahead, offer a great opportunity to inform and support farmers in their agro-management actions, e.g. on: planning of sowing, selection of optimal crop variety, planning of fertilisation and field interventions, disease treatment, and irrigation water use. Yet, these predictive systems are complex, difficult to interpret and not as accurate as a farmer would expect them to be. Hence, a new generation of Empirical SFS build on top of Copernicus Seasonal Forecasts and advanced Artificial Intelligence (AI) techniques is proposed by Geoskop.

PLANET – hyPerlocal cLimate driven LANd Evaluation (intelligent) Tool

Company: NEURALIO AI P.C
Country: Greece
Domain: Agriculture

Abstract: Making use of modern technologies and methodologies like Artificial Intelligence, Big Data Analytics and leveraging the wealth of geospatial earth observation data made available through the EU Copernicus and Eumetsat Agencies, PLANET aspires to develop an intelligent tool based on an automatic processing data chain that will offer a hyperlocal climate-driven land-use suitability service on-demand, available to everyone that is having the willingness to evaluate their land for various crops under different climatic regimes and climate change projection scenarios, combining Copernicus EO data with soil, crop and socioeconomic data.

FertiRec – Postcode based fertiliser rate recommendation system

Company: Spacenus GmbH
Country: Germany
Domain: Agriculture

Abstract: The Nitrogen (N) fertilisation rate recommendation is a decade old problem that yet to be solved in an efficient way. Existing technologies are either too expensive or time consuming. As a result, farmers make fertiliser rate decisions based on their experience, which is not data-driven and includes guesswork. The proposed project intends to provide a solution to the current service gaps. With the solution, a user can get a fertiliser rate recommendation, ahead of the season, by providing field boundary and crop type. This not only helps the farmer in fertilisation efforts, but also assists them in fertiliser purchasing decisions. We intend to make the service available for key crops in western European countries.

OPTIMAL – cOPernicus irrigaTION mAnagement tooLkit

Company: Xilbi Sistemas de Informacion SL
Country: Spain
Domain: Agriculture

Abstract: The proposed project is focused on the development of the cOPernicus irrigaTION mAnagement tooLkit – OPTIMAL, aimed at taking the interaction between the farmers, irrigation resources and their plantation fields to a new level. OPTIMAL will deliver an Artificial Intelligence (AI) based Decision Support System (DSS) which will allow farmers to maximise irrigation resources and empower stakeholders with improved tools for policy level planning. The OPTIMAL development will be initially focused in the intensive almonds production – a product with high demand and commercial growth, whose production strongly depends on irrigation and will be progressively generalised towards aiming at other types of crops. OPTIMAL will provide better, more streamlined and optimised irrigation management; protect the environment; maximise existing investments; reduce operation and management costs; reduce losses and improve profitability. 

LIVE4ENV – Reducing the environmental impact of livestock farming and optimising resources using satellite imagery, IoT and AI

Company: Digitanimal S.L.
Country: Spain
Domain: Agriculture

Abstract: Grasslands occupy 1/3 of the total global landmass and provide the feed base for extensive livestock farms. This interaction is crucial for the provision of ecosystem services such as climate change mitigation, biodiversity conservation and the provision of food products. The development of AI-based tools using EO data and IoT devices for monitoring the environmental impact of extensive livestock farms is crucial for the long-term conservation of these ecosystems and the reappraisal of livestock farmers’ duty. The main objective of the LIFE4ENV project is the development and validation of AI-based service for assessing the environmental impact of extensive livestock farming and generating recommendations for an improved environmental performance of farms by using multiple data sources, such as EO data and IoT devices. Currently there is no such a service in the market, however, the goals of the project are in line with the European Green Deal and the new Common Agricultural Policy (CAP). Therefore, the outcomes of the project will likely have a significant impact on farm management, policy-making and climate change mitigation.