NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping

Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, fundet by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched Sentinel-2 satellite imagery, OpenStreetMap road data, floating car data (traffic density and speed), and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. Two case studies, one located in an urban environment (Graz) and the other one in a rural region near Linz demonstrate the model’s applicability across contrasting settings. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current legislation. This approach provides a scalable tool for evidence-based environmental planning, health risk assessment, and policy support.