DeepCube H2020

DeepCube leverages advances in the fields of Artificial Intelligence and Semantic Web to unlock the potential of big Copernicus data. Our objective is to address new and ambitious problems that imply high environmental and societal impact, enhance our understanding of Earth’s processes, correlated with Climate Change, and feasibly generate high business value. To achieve this we bring mature and new ICT technologies, such as the Earth System Data Cube, the Semantic Cube, the Hopsworks platform for distributed Deep Learning, and a state-of-the-art visualisation tool tailored for linked Copernicus data, and integrate them to deliver an open and interoperable platform that can be deployed in several cloud infrastructures and high-performance computing, including DIAS environments. We then use these tools to develop novel Deep Learning pipelines to extract value from big Copernicus data. We implement a paradigm shift in the use of AI pipelines. DeepCube 1) develops novel DL architectures that extend to non-conventional data and problems settings, such as Interferometric SAR, social network data, and industrial data, 2) introduces a novel hybrid modeling paradigm for data-driven AI models that respect physical laws, and 3) opens-up the DL black box through Explainable AI and Causality. We showcase these in five Use Cases (UC), two business, two on earth system sciences, and one for humanitarian aid. These are: UC1: Forecasting localized extreme drought and heat impacts in Africa, UC2: Climate induced migration in Africa, UC3: Fire hazard short-term forecasting in the Mediterranean, UC4a: Automatic volcanic deformation detection and alerting and UC4b: Deformation trend change detection on PSI time-series for critical infrastructure monitoring, UC5: Copernicus services for sustainable and environmentally-friendly tourism. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004188.