Method of identifying urban heat islands by remote sensing based on big data

Abstract

In recent decades, the speed of construction, expansion, and development of smart cities by humans has been increasing faster, thanks to new technologies. However, alongside the fantastic development of new technologies also comes negative impacts such as climate change. One of the characteristics of climate change is the changing thermal comfort of humans living in the city. Heat stress is a feature of the urban heat island phenomenon. Congestion of airflows within the urban area causes localized hot areas, causing a temperature difference of 2-3 degrees between the areas inside the city and the suburbs. This study presents a method for determining surface temperature and the location of heat islands based on Google's cloud computing platform. The platform uses a combination of satellite imagery, Earth observation data, and machine learning algorithms to allow users to identify and measure changes in land use, ecosystems, and climate patterns at a global scale. Google Earth Engine enables researchers and organizations to process enormous amounts of data with short turnaround times. The method described in the article allows the analysis and retrieval of data in the large dataset of the Landsat 8 satellite. By determining the location of heat islands, the government, policymakers, and planners can develop plans to cope with the urban heat island phenomenon and improve the quality of life for residents.