Deep Neural Networks for Comprehensive Environmental Noise Estimation in European Cities

This paper presents a novel deep learning-based methodology for estimating environmental noise across diverse European cities, significantly enhancing the coverage and precision of noise assessment and management. Utilizing an extensive range of environmental, traffic, and satellite-based land monitoring datasets, our models achieve a comprehensive and dynamic understanding of noise pollution patterns. The proposed method recontextualizes noise estimation as an image classification problem, employing advanced data preprocessing techniques and deep convolutional neural networks (CNNs) to predict noise levels with high accuracy. Our approach demonstrates scalability and cost-effectiveness in continuous noise monitoring, delivering critical insights into the health and policy implications of environmental noise. By filling data gaps in compliance with the European Environmental Noise Directive, our work facilitates the development of robust noise reduction strategies and informed urban planning. The results underscore the transformative potential of deep learning in environmental monitoring and decision-making, contributing significantly to the discourse on sustainable and resilient urban development.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>