Starlab has been selected to take part in Phase 1 of AI4Cities, an EU-funded Pre-Commercial Procurement (PCP) project, aiming to help cities accelerate their transition towards carbon neutrality. The project is looking for AI solutions that can contribute to the reduction of greenhouse gas emissions in the fields of mobility and energy, two domains responsible for 82% of all greenhouse gas emissions in European cities.
Following AI4Cities’ Request for Tenders on 01.12.2020 Starlab was one of the selected companies to submit an application for AI4Cities mobility challenge, following a thorough evaluation by the AI4Cities Buyers Group. Starlab will now participate in the project’s Solution Design Phase, during which in the next three months they will work on a full plan for the development of a prototype.
Starlab will develop GreenAI, which is a tool that will help cities to promote active mobility by informing citizens about environmental factors that affect their daily commuting. GreenAI will be based on artificial intelligence, remote sensing and will leverage the data collecting efforts launched by cities across the EU.
The AI4Cities Buyers Group consists of lead procurer Forum Virium Helsinki (representing the city of Helsinki), Cap Digital (representing Paris Region), the city of Amsterdam, the city of Copenhagen, the city of Stavanger and the city of Tallinn. These six cities and regions have developed ambitious strategies and policy plans to become carbon neutral by – at latest – 2050. In AI4Cities they joined forces to go through a PCP process, aiming to procure non-market ready solutions which can help them accelerate their transition to carbon neutrality by utilising artificial intelligence and related enabling technologies – such as big data applications, 5G, edge computing and IoT. Their choice to focus on energy and mobility-related challenges is informed by the fact that these two domains are responsible for 82% of GHG emissions in Europe’s cities.
This is part of the AI4Cities project that has received
funding from the European Union’s Horizon 2020
Research and Innovation Programme under grant agreement No 871914.