Periodic Reporting for period 2 - MUSE (Measuring U-Space Social and Environmental Impact)
Période du rapport: 2024-06-01 au 2025-11-30
U-space will enable urban air mobility (UAM) on a wide scale, facilitating a variety of use cases (e.g. medical and emergency transport, delivery of goods, passenger transport services). Social acceptance and impact on citizens is not completely understood in this growing scenario. Current U-space performance frameworks include indicators to evaluate UAM’s environmental impact. However, they are still too generic and aggregated to properly capture the broad variety of impacts of UAM operations.
The goal of MUSE is to develop a set of performance indicators (PIs), methods and tools for the assessment of the impact of UAM operations on the liveability and quality of life in European cities. The project established the basis for a future U-space service that supports the minimisation of UAM’s negative social and environmental externalities.
This general goal can be translated into the following specific objectives:
1. To define a set of U-space social and environmental PIs able to capture the full range of UAM impacts on citizens’ quality of life.
2. To develop new methods and tools for the measurement and forecasting of the proposed KPIs.
3. To showcase and evaluate the capabilities of the new methods and tools developed by MUSE through their application to a set of case studies in one or more European cities.
4. To create the conditions for the transfer of the project results to the subsequent stages of the R&I cycle by outlining a new SESAR Solution, the MUSE U-space Environmental and Social Impact Assessment Framework, that serves as the basis for a future U-space service aimed at optimising the social and environmental performance of UAM operations.
MUSE produced four main results: (i) the MUSE U-space Environmental and Social Performance Framework, (iii) MUSE U-space Environmental and Social Impact Assessment Toolset; (iv) the Case Study Report; and (iv) the MUSE Solution Data Pack, which consolidates the Performance Framework and the Assessment Toolset into a SESAR Solution.
The nature itself of the solution provides measurements of the scale of UAM’s impact on citizens. Numerous indicators developed provide the number of people exposed to, for example, a certain noise level, or the cumulative exposure on a specific population group segmented by age, gender, or other socioeconomic factors. Since UAM operations are inherently local, the primary impact is felt in urban environments. With over 260 European cities projected to exceed 300,000 inhabitants, MUSE provides a vital framework for local governments to manage the integration of drone services into daily city life.
The goal of MUSE is to develop a set of performance indicators (PIs), methods and tools for the assessment of the impact of UAM operations on the liveability and quality of life in European cities. The project established the basis for a future U-space service that supports the minimisation of UAM’s negative social and environmental externalities.
This general goal can be translated into the following specific objectives:
1. To define a set of U-space social and environmental PIs able to capture the full range of UAM impacts on citizens’ quality of life.
2. To develop new methods and tools for the measurement and forecasting of the proposed KPIs.
3. To showcase and evaluate the capabilities of the new methods and tools developed by MUSE through their application to a set of case studies in one or more European cities.
4. To create the conditions for the transfer of the project results to the subsequent stages of the R&I cycle by outlining a new SESAR Solution, the MUSE U-space Environmental and Social Impact Assessment Framework, that serves as the basis for a future U-space service aimed at optimising the social and environmental performance of UAM operations.
MUSE produced four main results: (i) the MUSE U-space Environmental and Social Performance Framework, (iii) MUSE U-space Environmental and Social Impact Assessment Toolset; (iv) the Case Study Report; and (iv) the MUSE Solution Data Pack, which consolidates the Performance Framework and the Assessment Toolset into a SESAR Solution.
The nature itself of the solution provides measurements of the scale of UAM’s impact on citizens. Numerous indicators developed provide the number of people exposed to, for example, a certain noise level, or the cumulative exposure on a specific population group segmented by age, gender, or other socioeconomic factors. Since UAM operations are inherently local, the primary impact is felt in urban environments. With over 260 European cities projected to exceed 300,000 inhabitants, MUSE provides a vital framework for local governments to manage the integration of drone services into daily city life.
- Refined the GEMMA (trajectory generation) and DynaPyVTOL (flight mechanics) tools based on simulation feedback to improve trajectory accuracy.
- Automated the CARMEN noise emission tool for full-scenario loops and integrated NoiseModelling for urban drone fleet propagation. Developed a Python script for calculating and mapping eight acoustic indicators.
- Implemented a GIS-based Python pipeline to calculate visual pollution for any trajectory.
- Built a remote sensing pipeline using supervised learning and rule-based post-processing to detect individuals from VHR satellite imagery (Pléiades Neo and Maxar).
- Created and calibrated dynamic population probability maps by fusing GPS app data with MND, validating results against satellite pedestrian counts.
- Standardized the indicators' pipeline and deployed an interactive Streamlit dashboard (muse.ext.nommon.es).
- Confirmed platform utility through stakeholder consultations and prioritized case study scenarios for parcel and emergency delivery.
- Executed experiments for delivery use cases, analyzing results presented at the 2nd Stakeholders Workshop and a EUROCONTROL webinar.
- Integrated expert input to refine emergency scenarios and define future research directions.
- Automated the CARMEN noise emission tool for full-scenario loops and integrated NoiseModelling for urban drone fleet propagation. Developed a Python script for calculating and mapping eight acoustic indicators.
- Implemented a GIS-based Python pipeline to calculate visual pollution for any trajectory.
- Built a remote sensing pipeline using supervised learning and rule-based post-processing to detect individuals from VHR satellite imagery (Pléiades Neo and Maxar).
- Created and calibrated dynamic population probability maps by fusing GPS app data with MND, validating results against satellite pedestrian counts.
- Standardized the indicators' pipeline and deployed an interactive Streamlit dashboard (muse.ext.nommon.es).
- Confirmed platform utility through stakeholder consultations and prioritized case study scenarios for parcel and emergency delivery.
- Executed experiments for delivery use cases, analyzing results presented at the 2nd Stakeholders Workshop and a EUROCONTROL webinar.
- Integrated expert input to refine emergency scenarios and define future research directions.
Development of a comprehensive social and environmental performance framework, not existing today, including highly disaggregated indicators and high-accuracy measurement mechanisms based on new sources of big data and state-of-the-art data analytics techniques.
Enhancement of drone trajectory simulation, by combining the capabilities of two state-of-the-art tools to provide more realistic representations of drone trajectories in urban U-space: GEMMA, a kinematic simulation engine, and DynaPyVTOL, which adds the physics needed for noise computation.
Enhancement of noise emission and propagation models, by integrating the CARMEN and Noisemodelling tools to enable the creation of accurate noise map for each drone flyover, accounting for reflection, masking and diffraction by buildings.
Improving current methods for dynamic population mapping and exposure assessment based on data from personal mobile devices, which were evolved in two ways: (i) increasing the quality of activity characterisation, thanks to the fusion of Mobile Network Data with mobile apps GPS data and VHR Earth Observation data that help us distinguish indoor and outdoor activities in a more accurate manner; (ii) developing the required data processing pipelines to compute individual exposure diaries over time, which enable a better measurement of cumulative exposure.
Enhancement of drone trajectory simulation, by combining the capabilities of two state-of-the-art tools to provide more realistic representations of drone trajectories in urban U-space: GEMMA, a kinematic simulation engine, and DynaPyVTOL, which adds the physics needed for noise computation.
Enhancement of noise emission and propagation models, by integrating the CARMEN and Noisemodelling tools to enable the creation of accurate noise map for each drone flyover, accounting for reflection, masking and diffraction by buildings.
Improving current methods for dynamic population mapping and exposure assessment based on data from personal mobile devices, which were evolved in two ways: (i) increasing the quality of activity characterisation, thanks to the fusion of Mobile Network Data with mobile apps GPS data and VHR Earth Observation data that help us distinguish indoor and outdoor activities in a more accurate manner; (ii) developing the required data processing pipelines to compute individual exposure diaries over time, which enable a better measurement of cumulative exposure.