RapidAI4EO aims to advance rapid and continuous land monitoring with state-of-the-art AI solutions. Under the sponsorship of the European Union’s Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products.
New catalogues of near-daily temporal data will soon dominate the global archives, offering unprecedented possibilities for rapid and continuous land monitoring. However, there has been little exploration of artificial intelligence (AI) techniques to leverage the spatiotemporal dimension at scale.
Training data remain rare relative to the spatiotemporal sampling that is necessary to adequately capture natural and man-made phenomenology latent in these large volumes of high cadence data.
The project aims to establish the foundations for the next generation of continuous land monitoring applications. The project’s 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. Together, the project team will develop improved AI processes, with focus on high resolution and cadence, and provide critical training data to establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. By fusing satellite data from Copernicus and third party high-resolution sources, we will provide intensified monitoring of Land Cover (LC) and Land Use (LU), Land-Use Change and Forestry (LULUCF) at a much higher level of detail and temporal cadence than is possible today.
The project objectives include:
- Establish the foundations for the next generation of rapid cadence land monitoring applications and in particular for the Copernicus Land Use Land Cover (LULC) suite of products.
- Demonstrate the fusion of Copernicus high resolution satellite imagery and third party very high-resolution imagery.
- Provide intensified monitoring of Land Use, Land-Use Change and Forestry (LULUCF), at a much higher level of detail and cadence than it is possible today.
- 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.
Expected project results include:
- Open source remote sensing spatiotemporal Machine Learning training corpus, combining Sentinel-2 with 3m Planet imagery at 500,000 patch locations over Europe.
- New land monitoring prototype to demonstrate:
- monthly high resolution thematic change detection over large areas of the EU (June 2022)
- highly effective end-to-end process to monitor and update the CORINE land cover product, with emphasis on improved understanding of land use with faster update cycles and reduced maintenance costs (March 2023)
NODES team is responsible for validation of the new Copernicus Land Use Land Cover (LULC) suite of products, resulting from the project activities, and is embedded in the CLMS production chain.