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Elena Donini received the award for the 2017 Best Italian Theses on Remote Sensing by the Italian Chapter of the IEEE Geoscience and Remote Sensing Society. The award was presented by Prof Antonio Iodice, who is the chapter chair. The title of the thesis is “Simulation and Extraction of Lava Tubes Features from Radar Sounder Data of the Moon” (Advisors: Prof Lorenzo Bruzzone and Dr Francesca Bovolo).

ESA engineers tested A 1:18 scale model of the RIME antenna – Radar for Icy Moons Exploration (Principal Investigator: Prof. Lorenzo Bruzzone) together with a simplified model of the JUICE spacecraft in the ESTEC’s HERTZ facility. Several tests were performed, for different solar array orientations, with the objective of collecting a set of data that can be compared with the results obtained through the simulations and thus validate the design and verification approach.

Minuaturised JUICE spacecraft and RIME antenna during electromagnetic compatibility tests at ESA’s technical centre in the Netherlands. Credit: ESA–M. Cowan

The antenna measurements were performed by rotating the scaled model and the RIME antenna along two axes, in order to measure the electric near field on a spherical surface in the vicinity of the scaled model. The near field data were then processed to obtain the far field performance, which is of particular interest because it more closely resembles real antenna performance.
This test campaign was a proof of concept that enabled fine-tuning of all relevant parameters. At a later stage, the antenna’s performance will be simulated with a high-fidelity model of the spacecraft, which will include all possible features that can influence the antenna pattern. These results will serve as inputs for the RIME instrument team to derive the overall inflight performance of the instrument.

More info here.

In the meanwhile the photo of the RIME’s antenna test was featured as ESA Space Science Image of the Week.

The ESA project “Scientific Exploitation of Operational Missions (SEOM) Multitemporal Analysis (MTA)” (coordinated and developed by RSLab) successfully passed its Final Review. The project was about the development of automatic techniques for the analysis of the big data acquired by  Sentinel-2 multispectral system in in the context of three application and methodological areas:
(1) Time series analysis, focused on the processing of image time series for precision agriculture.
(2) Change detection and attribution, focused on the analysis of bi-temporal images for detection of forest disturbances.
(3) Land cover maps updating, focused on the use of recent images for unsupervised updating of thematic maps.
The output of the project was very good. For this reason ESA is funding one additional year of activities in order to apply the developed methods to the analysis of images at Italian country scale using european cloud computing infrastructures. For further details visit https://rslab.disi.unitn.it/projects/mta/.

RSLab presented its research activities on Earth Observation and Planetary Exploration at the MUSE Science Museum in Trento in the framework of the European Researchers’ Night 2018.

On Thursday September 27, Silvia Demetri defended his PhD thesis on “Remote Sensing-Based Channel Modeling and Deployment Planning for Low-Power Wireless Networks” (supervisors Gian Pietro Picco and Lorenzo Bruzzone). The committee awarded the PhD in Information and Communication Technology.
Congratulations to Silvia!

RSLab is the coordinator of an important new project of European Space Agency (ESA) funded in the framework of the Climate Change Initiative, which is the flagship scientific program of ESA. This is the first project of this important program that has an Italian leadership.

The focus of the project is to generate high resolution time series of land cover maps (10/30 meters) at subcontinental level using multispectral and SAR satellite remote sensing images acquired between 1992 and now. The maps will be generated automatically by developing a processing chain based on machine-learning classification approaches and data fusion methodologies. The processing chain will be implemented in cloud for the analysis of the huge amount of data (big data) available.

The maps produced will be given as input to climate models for studying the interactions of land cover and changes in land cover at detailed scale on the the climate evolution. The first phase of the project (3 years) will be focused on South America (Amazon), Central Africa (Sahel) and Siberia areas where the long time series of high resolution data can significantly enhance the understanding of the climate processes.

The project started in the past days with the kick-off meeting at the European Space Research Institute (ESRIN) of ESA in Frascati.

Project Web Site: https://climate.esa.int/en/projects/high-resolution-land-cover/

RSLab got a very important new project in the framework of the very challenging ICTcall of Horizon 2020 Research and Innovation Framework Programme of European Commission (average proposal success rate of 8%). The ExtremeEarth  project will concentrate on developing the technologies that will make Europe a pioneer in the area of Extreme Earth Analytics i.e., the Remote Sensing and Artificial Intelligence techniques that are needed for extracting information and knowledge out of the petabytes of  Copernicus satellite remote sensing Big Data.

Project abstract

Copernicus is the European program for monitoring the Earth. The geospatial data produced by the Sentinel satellites puts Copernicus at the forefront of the Big Data paradigm, giving rise to all the relevant challenges: volume, velocity, variety, veracity and value. ExtremeEarth concentrates on developing the technologies that will make Europe a pioneer in the area of Extreme Earth Analytics i.e., the Remote Sensing and Artificial Intelligence techniques that are needed for extracting information and knowledge out of the petabytes of Copernicus data. The ExtremeEarth consortium consists of Remote Sensing and Artificial Intelligence researchers and technologists with outstanding scientific track records and relevant commercial expertise. The research and innovation activities undertaken in ExtremeEarth will significantly advance the frontiers in Big Data, Earth Analytics and Deep Learning for Copernicus data and Linked Geospatial Data, and make Europe the top player internationally in these areas. The ExtremeEarth technologies will be demonstrated in two use cases with societal, environmental and financial value: the Food Security use case and the Polar use case. ExtremeEarth will bring together the Food Security and Polar communities, and will work with them to develop technologies that can be used by these communities in the respective application areas. The results of ExtremeEarth will be exploited commercially by the industrial partners of the consortium.

project website

The paper “A Novel Automatic Approach to the Update of Land-Cover Maps by Unsupervised Classification of Remote Sensing Images” by Claudia Paris, Lorenzo Bruzzone, Diego Fernandez-Prieto presented at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017) hold in July 2017 in Fort Worth (US) got the very prestigious 2018 Symposium Prize Paper Award. The paper was “judged to be of exceptional merit” and resulted the winner out of more than 1000 papers presented in the oral sessions at the symposium. The award was presented at the Banquet of IGARSS 2018 hold in Valencia (Spain) on July 26th, 2018. It consists of a certificate and a honorarium.

Daniele Marinelli, PhD student at RSLab, got the Second Place in the Student Paper Competition at the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018) hold in Valencia (Spain) on 23-27 June 2018. This is a very relevant result as 230 full papers were submitted to the student paper competition this year. The papers were analyzed by a special committee of highly recognized experts in the field. Ten finalists where selected that presented their work in a special session at IGARSS. Daniele got the award (which consists of a certificate and a honorarium) with the paper “Fusion of Multitemporal Lidar Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds” by D. Marinelli, C. Paris, L. Bruzzone.