Objective
The FLORA project aims to develop a comprehensive, multidimensional indicator dashboard (FLORA dashboard) that combines data from LiDAR, remote sensing, GIS, and human perception surveys to assess and enhance urban environments. The project seeks to address the need for a holistic tool that integrates diverse data sources to capture and score urban aesthetic perception, liveability, and health, ultimately aiding urban planners and public health officials in making data-driven decisions to improve quality of life in cities. Urban environments are complex, with multiple factorssuch as green spaces, traffic patterns, air quality, and building structuresaffecting the well-being of residents. Existing assessment tools are often limited to specific factors, lacking the ability to synthesize data from multiple sources into a comprehensive view of urban health and aesthetics.
The breakthrough innovation of FLORA lies in its ability to merge multiple data sources into a single, cohesive assessment platform. Key innovations include:
1. Multisource Data Integration: Combining LiDAR, remote sensing, and human perception data for a comprehensive understanding of urban environments.
2. Multidimensional Indicators: Creating integrated metrics for urban beauty, liveability, and health, facilitating targeted urban interventions.
3. Human-Centred Approach: Including subjective data from residents to ensure that assessments reflect lived experiences and community needs.
The FLORA dashboard will drive evidence-based policy-making and foster proactive urban health strategies. FLORAs innovative approach entails challenges, such as data integration and interdisciplinary collaboration, but also promises high gains in setting new standards for urban health assessment. The potential of FLORA to revolutionize urban planning and public health strategies through data-driven, human-centred insights makes it a high-impact project with significant societal benefits.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesearth and related environmental sciencesphysical geographycartographygeographic information systems
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
20122 Milano
Italy