AI and earth observation for financial markets

The SPATIAL project develops a proof-of-concept prototype for forecasting soybean futures contracts price movement. These price forecasts rely on the AI-based combination of Earth Observation data with financial markets data.

Small and medium sized organizations, active in soybean futures contract trading, can exploit the predictability of soybeans futures contract price movement of SPATIAL to improve their trading strategies, enhance risk management and increase trading profits.  

Project Innovation


Soybeans and soybean by-products are the most-traded agricultural commodities, comprising more than 10% of the total value of the global agriculture trade. At every stage of the production chain, the Soybean market participants, face potential financial losses due to the Soybean Futures Contracts Volatility. Predictability of soybeans futures contract price movement is very important to agricultural organizations, food companies and traders.

SPATIAL performs the 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 contracts price moves, to demonstrate the feasibility of the method, the benefits of integrating Copernicus EO products and to showcase the potential of such approach.

Team expertise

The SPATIAL solution builds upon

a) the expertise of the prime contractor, HYPERTECH S.A. (, in financial assets price forecasting through machine learning and predictive analytics models for financial asset prices prediction based on traditional and alternative data sources along with its multi-year expertise on financial markets dynamics and deep knowledge on the key factors affecting commodities prices

b) the expertise on the development and deployment of Space-based applications of NOA ( for estimating soybeans crop yields and production.

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