RapidAI4EO is advancing 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.

Stimulate the development of new spatiotemporal modelling applications with the release of the most comprehensive spatiotemporal datasets ever created


Implement and benchmark novel DL architectures for Change Detection that can realize the full potential of the new combined data sources


Demonstrate large scale thematic Change Detection with high temporal cadence using daily harmonized multisensor, multiscale imagery.


Demonstrate improved LULC classification using daily harmonized multisensor, multiscale imagery.

Closing the growing gap between data availability and data interpretability in Earth Observations.

The emergence of powerful new artificial intelligence methodologies must be matched by tools that enable the remote sensing community to exploit their full potential when applied to the anticipated increasing volumes, cadence and diversity of sensor data. As it has been proved in other fields where artificial intelligence is revolutionizing entire industries (e.g. the self-driving car, video processing, voice recognition, face recognition), the availability of large volumes of training data representative of the phenomenologies to be modeled is key to overcoming a fundamental obstacle. A related challenge is the issue of sensor interoperability.

The overarching goal is to establish the foundations for the next generation of Copernicus Land Use Land Cover (LULC) suite of products. This involves demonstrating vastly improved AI processes with focus on high resolution and cadence, as well as providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the scope of this project.


Revolutionary machine learning core technology and overall framework to enable a new spatiotemporal level of data exploitation and drive the next generation of Copernicus Land Monitoring solutions.


Higher accuracy for the monitoring of compliance with environmental policies, Improved measurements of land degradation, Improved management of forest resources


Improved robustness for food security, Improved monitoring of carbon
stocks, Increased level of transparency, More agile interventions

Satellite Earth Observations, when analyzed through artificial intelligence (AI) tools, can help us measure
progress on the SDGs

In September 2015 the United Nations adopted the 2030 Agenda to achieve a better and sustainable future for all. We have only a decade to meet its Sustainable Development Goals (SDGs), the most ambitious agenda for global change in UN history. The measurement of sustainability has been a topic of debate among researchers, policy makers and other stakeholders. The 17 SDGs and related targets have been designed to be monitored through a set of global indicators, 93 of which are designed to measure the environmental dimensions of “sustainable development.” Alarmingly, nearly five years into the agenda the UN Environmental Programme has found that we have little to no data on 68 percent of them.

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We believe that the future of EO is in fusion, harmonization, and interoperability of satellite imagery. Intensified monitoring leads to better understanding of land use and reduction of maintenance costs for all LC products.

Each of the RapidAI4EO partners is the leader in their respective fields

The RapidAI4EO projects brings together Planet Labs, the operator of the world’s largest fleet of Earth-imaging satellites, Vito, the main production center of the Copernicus Global Land Service, Vision Impulse, a recent spin-off of German Research Center for Artificial Intelligence, the International Institute for Applied Systems Analysis (IIASA), and Serco Italia, a worldwide service provider to governments, international agencies and industries, and operator of the ONDA DIAS platform.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.