What is the RefMap project?
RefMap is short for Reducing Environmental Footprint through transformative Multi-scale Aviation Planning. RefMap brings together 11 partners from 8 countries. RefMap aims to reduce the environmental impact of air travel for airlines and Unmanned Aerial Systems by creating a digital service that optimises flight trajectories on both micro and macro levels. By using environmental data, such as wind, noise, CO2, and non-CO2 emissions, RefMap's analytics platform can help airlines make more eco-friendly decisions. This will lead to stricter evidence-based green policymaking in the aviation sector and the development of new aviation business models in line with the EU's Green Agenda.
What are the RefMap use cases?
RefMap aspires to have an impact on the service offered by aviation operators active on the large scale, such as airlines, and small scale, such as drone operators. The stakeholders involved and benefited from the outcomes of RefMap also include airports, regulators, and policy-makers. The services that are envisioned to be enabled through our technology in the RefMap project are applied in the use cases of the project, both on large and small scales.
Use cases in Large Scale
· Help airlines support sustainable aviation practices by complying with new regulations
· Minimise citizens environmental impacts, while ensuring airport capacity expansion
· Discover new airport locations with minimum environmental impact
· Empower EU regulators to oversee environmental footprints from the aviation industry
· Help airlines support sustainable aviation practices while remaining competitive with other transport modes
Use cases in Small Scale
· Limit urban air mobility impact on urban and peri-urban areas
· Demonstrate candidate locations in inhabited areas where drones can operate
· Maximise services and civil protection under extreme weather events
· Enable the delivery of essential goods, complementing other forms of transportation
What is the motivation behind RefMap?
The combination of the climate, COVID-19, and energetic crises (the latter, derived from a geopolitical crisis) has accelerated a societal and political shift towards the need to develop more digital, intermodal, and sustainable air transport. We approach future scenarios where airliners will operate on an environment-neutral manner and share the skies with smaller air vehicles that are electrically powered and designed for broad purposes, such as freight UAS (Unmanned Aircraft Systems, commonly known as UAVs or drones). This will intensify current airspace use while exacerbating noise levels and incorporating UAS traffic management (U-Space in Ethe U), making its management complex and difficult to sustain and predict through the classic air traffic management technologies available today.
Additionally, the increasing world population densities and climate change are stressing the importance of developing an aviation business model that is not only more resilient but respectful of the environment and enabling sustainable last mile logistics, Urban Air Mobility, as well as regional and long hauls. through the use of Artificial Intelligence (AI) techniques like natural-language processing, pattern recognition, and machine learning algorithms, on devices, sensors linked with the Internet of Things, we can now reap immense benefits of big data over numerous fields. This is also the case in aviation.
All in all, we claim herein that ground-breaking AI techniques can be used to enable the quantification and (eventually) reduction of the environmental impact of aviation from a multimodal perspective.
What is the duration of RefMap?
RefMap started on February 1st, 2023, and is planned to end on January 31st, 2026. During its three-year duration, it is expected to significantly contribute to creating sustainable aviation across Europe. The kick-off meeting of RefMap took place in Stockholm, Sweden on March 14th and 15th, hosted by KTH, the project’s coordinator.
How are the RefMap activities funded?
RefMap is a Horizon Europe funded project under the HORIZON-CL5-2022-D5-01-13 topic. This Project has received funding from the European Union’s HORIZON Research and Innovation Programme under Grant Agreement number 101096698.