Proposal: There is a need, in urban area, for GIS-like tool for grid operators, urban planning decision makers, industries, aggregators for solar energy trading, citizen (PV self-consumption) and researchers. This GIS-tool is meant to provide an urban energy system modelling of distribution grids to plan, monitor and nowcast (i.e. and short term forecast) the spatiotemporal variability of the electric consumption on one hand and of the production of fleet of PV rooftop systems on the other hand.

Proposal: Using satellite data and derived measurements of water parameters and a model for shellfish growth, Planetek is delivering Rheticus® Aquaculture service, providing information to aquafarmers about mussels’ growth rates, consisting of weekly bulletins with indicators calculated by the algorithms. Within the e-shape project, by organizing pilots in different plants in the Mediterranean Sea with the collaboration of the aquafarmers associations, we intend to scale up the adoption of the Rheticus® Aquaculture service in the Mediterranean and worldwide markets. We will pursue it by codesigning the service with the end-users, and adapting it to the different needs that different areas will require.

Proposal: WindSight is a suite of novel EO-based products for characterization of the land surface roughness and topography anywhere in the world. Together with state-of-the-art flow modeling tools for wind energy planning and prospecting, WindSight can replace current procedures for land surface characterization, which are based on manual assessment, coarse global data sets, or costly airborne measurement campaigns.

Proposal: Planetek Italia is an Italian company, established in 1994, which employs 50 men and women, passionate and skilled in Geoinformatics, Space solutions, and Earth science. We provide solutions to exploit the value of geospatial data through all phases of data life cycle from acquisition, storage, management up to analysis and sharing. We operate in many application areas ranging from environmental and land monitoring to open-government and smart cities, and including defence and security, as well as Space exploration and satellite missions. The main activity areas are: Satellite, aerial and drone data processing for cartography and geo-information production; Continuous monitoring with satellite data of Earth’s surface, infrastructures, work sites, urban dynamics or marine coastal areas in support of decision-making and operational activities Design and development of Spatial Data Infrastructures (SDI) for geospatial data archive, management and sharing; Design and development of real-time geo-location based solutions, through positioning systems such as GPS/Galileo/GNSS and indoor location systems; Development of software for the satellite on-board data and image processing and for ground segment infrastructures.

Proposal: To provide park managers with more insight into the fire dynamics of this large park and two adjacent agropastoral areas, ZEBRIS proposed the use of the platform firemaps..net to provide frequent and timely updates of burned areas and near real time monitoring of fires. To assess greenhouse gas emissions, measuring biomass burning and smoke release rates with infrared satellites was proposed. Fire risk and vulnerability were to be analysed using satellite data of past fire events, land cover information, weather data and fire behaviour models. Analysis and reporting-ready statistics were to be done largely automated in the processing environment provided by firemaps.net and served through a web GIS interface or as downloadable raster and vector data.

Proposal: On behalf of several provinces NEO has monitored whether trees disappeared in areas that fall under the nature conservation law. The enforcement of this law is difficult, as areas are enormous and human resources limited. By monitoring the trees in an automated way NEO delivers only the most relevant changes to the client to follow up in the field. The nationwide tree database of the Boomregister that was developed by NEO is used as baseline. Satellite data is used as source for the yearly updates. We use deep learning algorithms to signal where trees have disappeared, and filter them to only keep the most relevant ones.