The Global Earth Monitor (GEM) project is addressing the challenge of continuous monitoring of large areas in a sustainable and cost-effective way. The goal of the project is to establish a new disruptive Earth Observation Data: Exploitation model which will dramatically enhance the utilisation of Copernicus data.
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GEM takes advantage of the large volumes of available EO, weather, climate and other non- EO data to establish economically viable continuous monitoring of the Earth. As part of GEM, the development of scalable and cost-effective solutions is being tested on various use-cases, which include also crop identification.
Read more about this in our article A Deep Learning Approach for Crop Type Mapping Based on Combined Time Series of Satellite and Weather Data.
News
It was a pleasure to participate in the European Space Agency’s Living Planet Symposium in Bonn, Germany in May 23-27. GEM was presented during the poster session on the third day. The Symposium with more than 4,700 registered atendees was a great opportunity to highlight the project's objectives, platform and use cases.
See the GEM poster here and the LPS22 page for more information.
On July 20, GEM was presented at IGARSS 2022 which is the flagship conference of the IEEE Geoscience and Remote Sensing (RS) Society GRSS and is aimed at providing a platform for sharing knowledge and experience on recent developments and advancements in geoscience and RS technologies, particularly in the context of Earth Observation (EO), disaster monitoring and risk assessment. GEM was presented in the “Addressing New Security scenarios with Big EO data” session: with the New Security concept, the paradigm of Security as isolated domain changes to a complex, inter-domain one. Climate Security issues are tackled in the Conflict Pre-Warning Map pilot within GEM: SatCen presentation from Michele Lazzarini entitled "New Scenarios shaping a Digital Twin Earth for Security" highlighted the contribution of GEM in shaping a Digital Twin Earth for Security, using heterogeneous data sources and machine learning techniques.
See the presentation here.
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