GEO-K is partner of ɸ-Sat-2! The ESA Artificial Intelligence Earth Observation Mission

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Following yesterday’s successful launch of ɸ-sat-1 (Europe’s first artificial intelligence Earth observation mission) plans are already underway for the next innovative state-of-the-art technology, ɸ-sat-2. It will demonstrate the capabilities of artificial intelligence (AI) technology for Earth observation. The use of these technologies will lead to new ways of collecting, distributing and analysing data about our planet.

GEO-K, as part of a consortium from six different European countries leded by Open Cosmos, developed an innovative solution for ɸ-sat-2 that has been selected as the winning idea by a panel of ESA experts. Find details on our contribution in projects section. 

Our  ɸ-sat-2 proposal involves an Earth observation 6U CubeSat platform capable of running AI apps that can be developed, easily installed, validated and operated on the spacecraft during their flight using a simple user interface.

Besides GEO-K, the consortium includes CGI, Ubotica, Simera CH Innovative, CEiiA and KP Labs.

Find more on ESA website.

EUMETSAT & CNR with GEO-K united by an Algorithm

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On 20 of May 2016, at the EUMETSAT Headquarters in Darmstadt, Germany, GEO-K signed a new contract for a study about cloud detection over sea ice using the “Sea and Land Surface Temperature Radiometer” (SLSTR) instrument aboard on Sentinel 3 satellite.

Sea ice is typically present on polar oceans of the planet and has a crucial role on climate. Therefore, monitoring its morphological structure, its changes and its temperature is important to have a clear picture of the actual and future climate state.

To do these kind of observations at best, it is important that the satellite view is completely free of clouds, then it becomes necessary an algorithm that can distinguish a white brilliant cloud from an ice surface also white and shiny. GEO-K, in collaboration with ISAC-CNR (Italian Institute of Atmospheric Science and Climate) as subcontractor, will supply such an algorithm.

The people involved in this project are: Massimiliano Sist (GEO-K), Lia Santoleri (ISAC-CNR) and Gianluigi Liberti (ISAC-CNR).

 

Thales Alenia Space Italia chooses GEO-K technology for SAR data analysis

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Thales Alenia Space Italia chooses GEO-K to realize new platforms for COSMO-SkyMed SAR data analysis.

The agreement signed on 22 March 2016 requires the creation of a first suite for automatic analysis application for oil spills, ship survey, coastline extraction and land cover classification. All of this will be detected by neural network technology developed by GEO-K over the past years.

Both GEO-K and TAS-I wish the just-signed agreement may be renewed in view of new potential available with COSMO Second Generation Data.

 

GEO-K takes part to the study for super-classification of multispectral data

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GEO-K participates to the study for the super-classification of Landsat Multispectral products. The study will be published on the IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing in the next period.

What is super-classification?

With it, we consider the ability to discriminate the different “materials” that form a single pixel signal. Often these materials are different and composite on the Earth’s surface. With this approach, we can ultimately increase the capability of satellite instruments in terms of spatial resolution.

The methodology proposed by GEO-K and the Earth Observation Laboratory of the University of Rome Tor Vergata is based on advanced techniques exploiting Neural Networks.

The first results obtained by the team were presented at the URBAN 2015 Remote Sensing Joint Event in Losanna, Switzerland.

URBANFLUXES at Rome 2015 Science Symposium on Climate

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GEO-K took part at the Rome 2015 Science Symposium on Climate poster session, on November 19-20, with a contribution to URBANFLUXES project.

The recently launched Horizon 2020 project URBANFLUXES investigates the potential of EO to retrieve urban energy budget components, focusing on the anthropogenic heat flux. The main challenge of this project is the innovative exploitation of the Copernicus Sentinels synergistic observations to estimate local scale spatio-temporal patterns of the anthropogenic heat emission in cities. These EO-based spatially disaggregated estimations contain valuable information for both the urban planning and the Earth System Science community.