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Marco Chini

Scientist, LIST (Luxembourg Institute of Science and Technology)

Marco Chini earned the M.Sc. degree in electronic engineering from the Sapienza University of Rome, Italy, and the Ph.D. degree in geophysics from the University of Bologna, Italy. He is a lead remote sensing scientist at the Luxembourg Institute of Science and Technology (LIST), where he is responsible for acquiring, managing and developing research and innovation projects focusing on remote sensing, deep learning, classification and geophysical parameter estimation. The projects he is involved in, both fundamental and applied in nature, focus on understanding natural and anthropic phenomena at large scale with use cases spanning from floodwater detection, natural disasters monitoring, urban sprawl mapping, humanitarian aid, maritime surveillance and defence. 

AI- and EO-based capability to get fast access to large scale transboundary information. 

The availability of Earth Observation (EO) satellite systems has increased significantly in the last two decades in response to a growing demand from many end users and operational service providers from different sectors. Important satellite constellations equipped with advanced and diversified sensors continue to be developed and already now provide the capacity to monitor planet Earth more frequently and more precisely and with a higher level of detail than ever before.  This large amount of new Earth Observation data is transforming the way products can be provided, the way operational EO-based services are deployed in many domains, and it is opening up to have easily and remotely access to cross-border information, which is intrinsic to satellite data. In this respect, the Copernicus program, which is the European Union's Earth observation programme, coordinated and managed by the European Commission in partnership with the European Space Agency (ESA), has been a game changer, as it is able to provide a global, continuous, autonomous and high quality Earth observation capacity. Providing accurate, timely and easily accessible information helps improving the management of the environment, to understand and to mitigate the effects of climate change, and to contribute to security. 

This deluge of satellite data to international authorities has no operational value per se. Hence, the impellent need to rapidly process, analyse, and transform the raw EO data in order to generate intuitive, understandable and quantitative measurements of geopolitical relevance. It goes without saying that this effort largely benefits of recent advances in artificial intelligence (AI) and machine learning approaches as they make it possible to efficiently explore large data sets and to bring new insights to many application fields, such as migration, drought, water scarcity, maritime surveillance, natural disasters, humanitarian aid and defence. Even though there are still challenges to face such as biases in remote sensing data and the difficulty of making models robust and scalable, EO & AI represents a tandem of technologies that can be truly disruptive. Without being impacted by any geographical barriers, the integration of both technologies enables an automated and efficient monitoring of our constantly changing planet and provides thus important information for political decision taking.   

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