A Comparative study on the impact of urbanisation on microclimate in Cairo (Egypt) and London (UK) using remote sensing and Machine Learning - L. Sabobeh; T. Ali; M. Md. Mortula (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
Urbanization significantly affects local microclimates, contributing to the urban heat island (UHI) effect, particularly in rapidly expanding cities. Effective monitoring of these changes is crucial for sustainable urban planning and climate adaptation. This study presents a comparative analysis of the impact of urbanization on the microclimates of two large, socio-economically distinct cities—Cairo, Egypt, and London, UK—between 2000 and 2023. Cairo's rapid, unplanned urban expansion contrasts with London's more regulated, gradual growth, providing an opportunity to assess how different urbanization patterns and climates influence UHI effects. Using Landsat Collection 2 satellite imagery and Google Earth Engine (GEE) for classification, Land Use and Land Cover (LULC) was divided into four categories: water bodies, vegetation, developed areas, and barren land. Several machine learning (ML) algorithms were compared, with Support Vector Machine (SVM) ultimately selected for its superior performance. The classified data were further analysed in ArcGIS Pro. The results show a 45% increase in developed land and a 38% reduction in vegetation in Cairo, leading to an average LST increase of 5°C. London experienced a 25% increase in developed areas and a 20% reduction in green spaces, with LST rising by 2.5°C. The study achieved an overall classification accuracy of 0.89 and a kappa coefficient of 0.85, demonstrating the effectiveness of SVM across both cities with differing climates. This research contributes to urban sustainability efforts by identifying the best ML approach for monitoring LULC changes in distinct global cities, offering insights for data-driven urban planning and UHI mitigation.