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.