Search

ExtremeEarth is a three-year H2020 ICT research and innovation project. Its main objective is to develop Artificial Intelligence and big data technologies that scale to the large volumes of big Copernicus data, information and knowledge, and apply these technologies in two of the European Space Agency (ESA) Thematic Exploitation Platforms (TEP): Food Security and Polar.

ExtremeEarth is a three-year H2020 ICT research and innovation project. Its main objective is to develop Artificial Intelligence and big data technologies that scale to the large volumes of big Copernicus data, information and knowledge, and apply these technologies in two of the European Space Agency (ESA) Thematic Exploitation Platforms (TEP): Food Security and Polar.

Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques., Comment: 18 pages, 10 figures, submitted to Pattern Recognition

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

In this paper, the retrieval of sea-ice surface heights from the interferometric TanDEM-X data is investigated. The data were acquired over fast and drifting ice in Fram Strait located between Greenland and Svalbard. Additional measurements of the sea-ice surface topography were carried out using a stereo camera and a laser altimeter. The comparison of the surface elevation retrieved from TanDEM-X imagery with the results of the stereo camera measurements revealed that sea ice ridges greater than 0.5 m can be estimated with a root mean-square error of 0.3 m or less with the error decreasing as a function of ridge height. Although the helicopter-borne laser data are only available as 1-D profiles with a much higher across-track spatial resolution than the TanDEM-X data, they proved to be useful for the validation. The need for multilook averaging to reduce the phase noise is identified as the main challenge in achieving the spatial resolution necessary for retrieving sea-ice surface topography using synthetic aperture radar interferometry.

In this paper, the retrieval of sea-ice surface heights from the interferometric TanDEM-X data is investigated. The data were acquired over fast and drifting ice in Fram Strait located between Greenland and Svalbard. Additional measurements of the sea-ice surface topography were carried out using a stereo camera and a laser altimeter. The comparison of the surface elevation retrieved from TanDEM-X imagery with the results of the stereo camera measurements revealed that sea ice ridges greater than 0.5 m can be estimated with a root mean-square error of 0.3 m or less with the error decreasing as a function of ridge height. Although the helicopter-borne laser data are only available as 1-D profiles with a much higher across-track spatial resolution than the TanDEM-X data, they proved to be useful for the validation. The need for multilook averaging to reduce the phase noise is identified as the main challenge in achieving the spatial resolution necessary for retrieving sea-ice surface topography using synthetic aperture radar interferometry.

Compared to widely used C-band SAR imagery, L-band SAR is a valuable complement in monitoring the Arctic and Antarctic sea ice covers, since it is very sensitive to ice deformation structures. The continuous observation of sea ice and the retrieval of sea ice properties from satellite data has received increased attention from the science community after the extreme minimum of summer arctic sea ice extent observed in 2007, which was followed by an even lower minimum in 2012. Also in operational sea ice monitoring there is an interest in investigating the potential of L-band SAR for sea ice mapping, although spatial coverage and timeliness of L-band data provided by recent satellite missions are still a critical issue for operational services. The concept of the TANGOSat mission will it make possible to address the potential of satellite tandems in gaining additional insights into the interaction between longer microwaves and sea ice. In particular the tomographic and bistatic phase may offer possibilities for specific investigations such as depth of scatterers and structures in thick multi-year ice (as found north of Greenland), quantification of ice surface roughness due to the occurrence of deformation structures, potential improvements of ice type classification, or snapshots of ice drift components by using along-track interferometry. In the presentation, different possibilities of investigations will be discussed.

Compared to widely used C-band SAR imagery, L-band SAR is a valuable complement in monitoring the Arctic and Antarctic sea ice covers, since it is very sensitive to ice deformation structures. The continuous observation of sea ice and the retrieval of sea ice properties from satellite data has received increased attention from the science community after the extreme minimum of summer arctic sea ice extent observed in 2007, which was followed by an even lower minimum in 2012. Also in operational sea ice monitoring there is an interest in investigating the potential of L-band SAR for sea ice mapping, although spatial coverage and timeliness of L-band data provided by recent satellite missions are still a critical issue for operational services. The concept of the TANGOSat mission will it make possible to address the potential of satellite tandems in gaining additional insights into the interaction between longer microwaves and sea ice. In particular the tomographic and bistatic phase may offer possibilities for specific investigations such as depth of scatterers and structures in thick multi-year ice (as found north of Greenland), quantification of ice surface roughness due to the occurrence of deformation structures, potential improvements of ice type classification, or snapshots of ice drift components by using along-track interferometry. In the presentation, different possibilities of investigations will be discussed.

This paper summarizes the knowledge status in some areas of SAR monitoring of sea ice. It starts with a brief summary of the whitepaper by Breivik et al. from OceanObs’09 [3], and then focuses on segmentation and classification, drift estimation, and assimilation strategies, which are considered as key areas in the development of more mature sea ice products from SAR and polarimetric SAR (PoLSAR) data.

This paper summarizes the knowledge status in some areas of SAR monitoring of sea ice. It starts with a brief summary of the whitepaper by Breivik et al. from OceanObs’09 [3], and then focuses on segmentation and classification, drift estimation, and assimilation strategies, which are considered as key areas in the development of more mature sea ice products from SAR and polarimetric SAR (PoLSAR) data.