HeatWatch: Identification of Parameters Influencing the Urban Heat Island Effect through Deep Learning Techniques

Abstract

Global climate change (GCC) is accelerated by factors such as greenhouse gas emissions from human activities and the urban heat island (UHI) effect, particularly in densely urbanized areas. According to the WMO's 2023 data, GCC warming effects increased by 49% from 1990s to 2021, 80% of this increase was due to CO2. Furthermore, average global temperatures have increased by 1.1°C since the early 1900s. In this respect, the urban heat island (UHI) effect has gained importance with global temperatures. Environmental problems that cause cities to be warmer than rural areas, especially due to hard ground surfaces, building density and decreasing green areas, have the potential to create negative impacts on human health. Therefore, it is important to identify and manage the impact of UHI. This is because traditional methods are limited to fixed station data, but technologies such as remote sensing and geographic information systems (GIS) provide more comprehensive results for this management. In an innovative approach, deep learning and artificial intelligence techniques can provide more accurate analysis by processing large datasets. In this context, this research proposes a conceptual framework for using deep learning techniques to detect the UHI effect with data obtained from street images and unmanned aerial vehicles. With the proposal, the UHI value will be calculated by detecting objects such as trees, air conditioners, vehicles and building cladding with the YOLO-Real-Time Object Detection algorithm. With this approach, it is aimed to obtaining more precise and accurate results in determining the UHI effect. In addition, a web-based management panel will be designed for managers to review the results and use them in decision-making mechanisms. It is aimed at disseminating this model and making it an important tool in the planning of urban areas.