Agriculture plays a central role globally by providing food for humans and livestock or material for industrial processes. It is a driver of economic growth and will play an essential role in reducing the impact of climate change and greening our economies. Accurate and reliable agricultural data is therefore paramount to ensure food security and global subsistence.

The goal of this challenge is to map cultivated land using Copernicus Sentinel imagery, and to develop solutions to extract as much information as possible from the native 10-meter per pixel resolution.

Your aim will be to identify agricultural areas smaller or narrower than a Sentinel-2 pixel. The participants are requested to produce output datasets on one area of interest in Slovenia.

This challenge was designed in collaboration with the Data Science in Earth Observation department at TUM/DLR.

Check it out at https://platform.ai4eo.eu/enhanced-sentinel2-agriculture