[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121407-en":3,"doc-seo-121407-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121407,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of Urban Environments Using State-of-the-Art Machine Learning - A Path to Sustainability - Proceedings Paper","Urban green infrastructure is essential to sustainable city development, yet expanding urban areas increasingly degrade vegetation and elevate public health risks. New development pressures reduce green spaces and create environmental stressors that threaten air quality and residents’ thermal comfort. This work leverages remote sensing and publicly available large-scale data with machine learning to classify urban landscapes using Sentinel-2 imagery, extracting meaningful features. The goal is a robust, scalable monitoring framework that supports timely decisions for sustainable urban growth.","UWL REPOSITORY  \n[repository.uwl.ac.uk](repository.uwl.ac.uk)  \nClassification of urban environments using state-of-the-art machine learning: a  \npath to sustainability  \nTemtime Tessema, Tesfaye ORCID logoORCID: [https://orcid.org/0000-0001-6577-446X](https://orcid.org/0000-0001-6577-446X), Azarmehr, Neda ORCID logoORCID: [https://orcid.org/0000-0002-6367-207X](https://orcid.org/0000-0002-6367-207X), Saadati, Parisa, Mortimer, Dale and Tosti, Fabio ORCID logoORCID: https://orcid.org/0000-0003-0291-9937 (2025) Classification of urban environments using state-of-the-art machine learning: a path to sustainability. Engineering Proceedings, 94 (1).  \n[https://doi.org/10.3390/engproc2025094014](https://doi.org/10.3390/engproc2025094014)[ ](https://doi.org/10.3390/engproc2025094014)[This is the Published Version of the final output.](This is the Published Version of the final output.)  \nUWL repository link: [https://repository.uwl.ac.uk/id/eprint/13941/](https://repository.uwl.ac.uk/id/eprint/13941/)  \nAlternative formats: If you require this document in an alternative format, please contact:  \n[open.research@uwl.ac.uk](open.research@uwl.ac.uk)  \nCopyright: Creative Commons: Attribution 4.0  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy: If you believe that this document breaches copyright, please contact us at [open.research@uwl.ac.uk](open.research@uwl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nProceeding Paper  \nClassification of Urban Environments Using State-of-the-Art Machine Learning: A Path to Sustainability †  \nTesfaye Tessema 1,2, *, Neda Azarmehr 3, Parisa Saadati 1,2, Dale Mortimer 4 and Fabio Tosti 1,2  \nAcademic Editor: GiorgosMallinis  \nPublished: 4 August 2025  \nCitation: Tessema, T.; Azarmehr, N.; Saadati, P.; Mortimer, D.; Tosti, F. Classification of Urban Environments  \nUsing State-of-the-Art Machine Learning: A Path to Sustainability.  \nEng. Proc. 2025, 94, 14. [https://](https://)[ ](https://)[doi.org/10.3390/engproc2025094014](doi.org/10.3390/engproc2025094014)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Computing and Engineering, University of West London, St Mary’s Road, Ealing, London W5 5RT, UK; [parisa.saadati@uwl.ac.uk](parisa.saadati@uwl.ac.uk) (P.S.); [fabio.tosti@uwl.ac.uk](fabio.tosti@uwl.ac.uk) (F.T.)  \n2 The Faringdon Research Centre for Non-Destructive Testing and Remote Sensing, University of West London, St Mary’s Road, Ealing, London W5 5RT, UK  \n3 Information School, The University of Sheffield, Sheffield S10 2TN, UK; n.azarmehr@sheffield.ac.uk  \n4 Tree Service, London Borough of Ealing, London W5 2HL, UK; [mortimerd@ealing.gov.uk](mortimerd@ealing.gov.uk)  \n* Correspondence: [tesfaye.temtimetessema@uwl.ac.uk](tesfaye.temtimetessema@uwl.ac.uk)  \n† Presented at the International Conference on Advanced Remote Sensing—Shaping Sustainable Global Landscapes (ICARS 2025), Barcelona, Spain, 26–28 March 2025 .  \nAbstract  \nUrban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicl","cbCaijy4WtUSNGs9","https://ap.wps.com/l/cbCaijy4WtUSNGs9","pdf",5867279,1,9,"English","en",105,"# Introduction\n## Urban green infrastructure and sustainability\n## Monitoring urban landscapes with remote sensing\n# Method Overview\n## Sentinel-2 classification of urban environments\n# Expected Outcomes\n## Scalable framework for monitoring green spaces\n# Keywords and Focus","[{\"question\":\"Why is urban green infrastructure critical for sustainability?\",\"answer\":\"It supports sustainable development by alleviating urban stressors such as air pollution and the urban heat island effect, which affects residents’ wellbeing.\"},{\"question\":\"What challenge does the paper address?\",\"answer\":\"Urban expansion and development pressure reduce vegetation and green spaces, increasing environmental impacts and public health risks.\"},{\"question\":\"How does the proposed approach improve monitoring?\",\"answer\":\"It uses Sentinel-2 remote sensing and publicly available large-scale data combined with machine learning to classify urban environments and extract meaningful features.\"}]","Classification of Urban Environments Using State-of-the-Art Machine Learning - 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