Temtime Tessema, Tesfaye ORCID: https://orcid.org/0000-0001-6577-446X, Azarmehr, Neda ORCID: https://orcid.org/0000-0002-6367-207X, Saadati, Parisa, Mortimer, Dale and Tosti, Fabio ORCID: 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).

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Abstract

Urban 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.
Publicly available large-scale data, combined with machine learning models, can
improve our understanding. We explore the potential of Sentinel-2 to classify and extract
meaningful features from urban landscapes. Using advanced machine learning techniques,
we aim to develop a robust and scalable framework for classifying urban environments.
The proposed models will assist in monitoring changes in green spaces across diverse
urban settings, enabling timely and informed decisions to foster sustainable urban growth.

Item Type:	Article
Identifier:	10.3390/engproc2025094014
Keywords:	machine learning; urban green infrastructure; remote sensing; sustainability
Subjects:	Construction and engineering
Depositing User:	Tesfaye Temtime Tessema
Date Deposited:	05 Aug 2025 10:25
Last Modified:	05 Aug 2025 10:45
URI:	https://repository.uwl.ac.uk/id/eprint/13941

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