The RapidAI4EO dataset is revolutionary in both its spatiotemporal dimension and quality (multisource, harmonised, cloud-free, analysis ready). Before its advent, there were no available annotated large scale data sets that provided the high cadence, multi year time series necessary to drive the development of new AI architectures that can fully exploit the new data streams provided by multiple constellations.
The current CLC product has a Minimum Mapping Unit of 25 ha and 100m resolution. We have developed LC mapping and change detection solutions that can improve the spatial resolution of the product by 10-20x. The temporal updates of the current product are every 6 years and our solution can efficiently drive quarterly updates (quarterly heat maps of change, up to monthly land cover maps).
RapidAI4EO enables more accurate measurements from space in support of several of the SDGs thanks to the much higher temporal cadence and spatial resolution. It can provide change detection maps for the entire European continent which has an enormous potential for various sectors, enabling continuous environmental monitoring, monitoring of urban expansion, early alerts for deforestation, ploughing of protected permanent grasslands, and other abrupt or gradual environmental changes. Having an automated mapping approach to map different types of urban tissue or forest types, can be a game changer and help countries to automatically update their cadaster or better determine and monitor the amount of woody biomass and assess their carbon stock. Delivering continuous observation and mapping capabilities has an enormous potential for further scientific discoveries and to understand, anticipate and address the potential consequences of human activities on the planet and its climate. There is already evidence that high cadence, high resolution EO measurements have led to the discovery of previously unknown phenomenology.
