Our planet's population is growing rapidly, while climate change is causing more extreme weather events and changes in land-use. This creates a demand for better information services in policy-making, nature conservation, and food security.
The European Commission's Common Agricultural Policy (CAP) urges member states to embrace Earth Observation (EO) data as an integral part of the area monitoring system (AMS). At the same time, the carbon emission trading sector is growing, making it essential to monitor different agricultural practices. This project aims to address these pressing needs by enhancing existing artificial intelligence (AI) models and developing new ones. Primarily, we seek to support agricultural paying agencies in their transition to the AMS, where they have to rely mainly on satellite-based monitoring in agricultural checks. Furthermore, our AI-based tillage detection will play an important role in promoting sustainable agriculture as anticipated in the European Green Deal, distinguishing and rewarding low-impact practices that contribute to carbon sequestration. By combining AI and satellite data, this project represents a transformative step towards a greener and more informed agricultural landscape.
The project is funded by the European Space Agency.
1 Analyse the ability to detect conservation tillage and, if feasible, develop separate AI models for conventional and conservation tillage detection.
2 Train AI models for parcel-based tillage detection in parallel with 6-day and 12-day coherence input. Incorporate Landsat satellite data alongside Sentinel-2 to provide denser optical imagery time series.
3 Comprise various countries in the Baltic Sea region to develop a versatile tillage detection model. Validate the model in collaboration with the paying agencies.
The project lasts from September 2023 till September 2024.
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