Assessing the feasibility of applying machine learning tools to predict environmental stressors from digital urban fingerprints
Assessing the feasibility of applying machine learning tools to predict environmental stressors from digital urban fingerprints
Urban noise pollution poses persistent challenges to public health and urban sustainability. This dissertation advances a new technical paradigm for scalable urban noise prediction by integrating multispectral remote sensing imagery, land use/land cover (LULC) data, and state-of-the-art machine learning techniques. The research adopts a progressive structure comprising three data-driven studies, each representing a major methodological step forward.
Chapter 4 presents a pioneering approach that leverages convolutional neural networks (CNNs) to predict citywide noise levels using high-resolution multispectral imagery, validated in Southampton. Chapter 5 incorporates geospatial relationships through graph-based modeling, further improving spatial prediction accuracy. Building on these foundations, Chapter 6 proposes a generalizable dual-branch graph neural network (GNN) framework with domain adaptation and pseudo-labeling, enabling robust noise mapping across five UK cities using standardized remote sensing and Urban Atlas LULC data.
Results demonstrate that deep learning models—when properly integrated with remote sensing and urban structural features—can achieve high accuracy and transferability in noise prediction, even in cities lacking extensive noise measurements. The workflow substantially reduces field data collection costs and advances urban noise assessment toward scalable, transferable solutions.
This dissertation thus bridges the gap between traditional acoustic modeling and next-generation data-driven mapping, providing methodological innovations with practical value for urban planners and environmental authorities seeking efficient, city-scale noise management tools.
urban noise, deep learning, domain adaptation, GNN, CNN, strategic noise maps, noise prediction, remote sensing imagery, LULC, data driven research
University of Southampton
Zhu, Feiyu
f3a5f689-3e92-4d9c-9fc4-eca430092d12
2025
Zhu, Feiyu
f3a5f689-3e92-4d9c-9fc4-eca430092d12
Eigenbrod, Felix
43efc6ae-b129-45a2-8a34-e489b5f05827
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Zhu, Feiyu
(2025)
Assessing the feasibility of applying machine learning tools to predict environmental stressors from digital urban fingerprints.
University of Southampton, Doctoral Thesis, 296pp.
Record type:
Thesis
(Doctoral)
Abstract
Urban noise pollution poses persistent challenges to public health and urban sustainability. This dissertation advances a new technical paradigm for scalable urban noise prediction by integrating multispectral remote sensing imagery, land use/land cover (LULC) data, and state-of-the-art machine learning techniques. The research adopts a progressive structure comprising three data-driven studies, each representing a major methodological step forward.
Chapter 4 presents a pioneering approach that leverages convolutional neural networks (CNNs) to predict citywide noise levels using high-resolution multispectral imagery, validated in Southampton. Chapter 5 incorporates geospatial relationships through graph-based modeling, further improving spatial prediction accuracy. Building on these foundations, Chapter 6 proposes a generalizable dual-branch graph neural network (GNN) framework with domain adaptation and pseudo-labeling, enabling robust noise mapping across five UK cities using standardized remote sensing and Urban Atlas LULC data.
Results demonstrate that deep learning models—when properly integrated with remote sensing and urban structural features—can achieve high accuracy and transferability in noise prediction, even in cities lacking extensive noise measurements. The workflow substantially reduces field data collection costs and advances urban noise assessment toward scalable, transferable solutions.
This dissertation thus bridges the gap between traditional acoustic modeling and next-generation data-driven mapping, providing methodological innovations with practical value for urban planners and environmental authorities seeking efficient, city-scale noise management tools.
Text
final submission_A
- Version of Record
Text
Final-thesis-submission-Examination-Mr-Feiyu-Zhu
Restricted to Repository staff only
More information
Submitted date: 17 November 2025
Published date: 2025
Keywords:
urban noise, deep learning, domain adaptation, GNN, CNN, strategic noise maps, noise prediction, remote sensing imagery, LULC, data driven research
Identifiers
Local EPrints ID: 506911
URI: http://eprints.soton.ac.uk/id/eprint/506911
PURE UUID: b2d3f5ca-6e77-49a4-acc3-5f1e4d0f92c9
Catalogue record
Date deposited: 20 Nov 2025 17:33
Last modified: 22 Nov 2025 03:00
Export record
Contributors
Thesis advisor:
Jonathon Hare
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics