Meet the winners of the 3rd Open Call for Use cases based on citizen-driven social challenges, with beneficiaries consortia of high-tech & at least 1 low-tech SME, startups, spin-offs and a total funding up to €150.000 (3-partner consortia) or up to €120.000 (2-partner consortia).

Urbalytics

Companies: Latitudo 40 / LAND Italia
Countries: Italy
Domain: Other (Urban Monitoring and Planning)

Abstract: The proposed experiment aims at demonstrating the possibility of applying machine/deep learning algorithms on Sentinel 2 images in order to estimate the Land Surface Temperature and, combining such information with land cover maps and meteorological data, to provide a clear indication of the urban areas affected by the Urban Heat Island phenomenon, of the temporal evolution of this phenomenon and, finally, to suggest a selection of Nature Based Solutions aimed at mitigating the phenomenon. The experiment will be conducted by analysing the territories of the cities of Naples and Milan.


AI4 E2O.GreenAI4 E2O.Green – from Earth Observation 2 Urban Irrigation AI Optimisation for urban green space overheating and greenhouse gas reduction

Companies: 3D EXECUTIVE MANAGEMENT SYSTEM / LIST GEOINFORMATIKA / Profida
Countries: Croatia
Domain: Agriculture

Abstract: AI4 E2O.Green will develop an Intelligent next gen deep/green tech Platform powered by AI to enable Urban Green and Golf Space Management Companies to effectively manage irrigation, assets, operations and land fields with a powerful combination of satellite and drones imagery as well as the AR and computer vision connected models. Our vision is to bridge the gap from Earth Observation 2 Energy Optimisation and bring next gen AI, IoT and Remote Sensing supported Green Space Irrigation Management to every Green Space in EU and beyond, in order to foster the rise of climate-neutral cities while making Copernicus data an industry benchmark for sustainable irrigation and energy optimisation of aforementioned green surfaces.


AI-RON MANAI-based wildfiRe predictiON for the risk MANagement of TLC Infrastructures

Companies: STAM S.r.l. / Gter S.r.l. / La SIA S.p.A
Countries: Italy
Domain: Security

Abstract: The AI-RON MAN project aims to deliver a digital tool to dynamically predict wildfire risk in rural areas where TLC infrastructures are located, supporting first responders to preventively intervene and avoid disruption of communication service. Such tool is expected indeed to significantly improve fire risk prediction capabilities, as well as preparedness of first responders and resilience of critical infrastructures. For this purpose, forecasts will be produced for a time horizon sufficient for infrastructure managers to put in place (or at least prepare) proper countermeasures and at the same time suitable to consider predictions reliable, ideally the next 48 hours. AI-RON MAN tool will also assess the network outage due to damages caused by wildfire, estimating likelihood and impact in terms of coverage area and potential number of users affected.