RapidAI4EO - Advancing the State-of-the-Art for Rapid and Continuous Land Monitoring

New catalogues of nearly daily temporal data will soon dominate the global archives.

However, there has been little exploration of Deep Learning (DL) techniques to leverage the spatiotemporal dimension at scale.

Training data remains rare relative to the spatiotemporal sampling which is necessary to adequately capture natural and man-made phenomenology latent in these large volumes of high cadence data. The project will establish the foundations for the next generation of rapid cadence land monitoring applications by:1.

Creating the most complete and dense spatiotemporal training set, combining Sentinel-2 with high cadence, very high resolution, harmonized multispectral Planet imagery at 500,000 patch locations over Europe, and open sourcing these datasets for the benefit of the entire remote sensing community.2.

Developing and benchmarking alternative ways of detecting and classifying change from very high cadence observations by training state-of-the-earth multiscale supervised and unsupervised DL classifiers on these unique data sources.3. Delivering high cadence high resolution change detection heatmaps for the entire European continent.4.

Demonstrating a highly effective end-to-end process to monitor and update the CORINE land cover product, with emphasis on improved understanding of land use, speeding up update cycles and reducing maintenance costs.Our framework constitutes a game changer in the ability to derive time-critical and location-specific insights into dynamic land surface processes.

Our ambition is to enable new and better ways of measuring and understanding the human footprint on our planet, which is a key challenge of the UN Sustainable Development Goals.

This project brings together industry leaders with a strong, demonstrated record of disruptive innovations and young innovators: Planet Labs, Vision Impulse, VITO, IIASA and ONDA DIAS/Serco Italia.