Detecting and classifying rooftops with a CNN-based remote-sensing method for urban area cool roof application

Abstract

Cool roofs reduce the greenhouse effect and increases the energy efficiency of buildings in urban areas, so they have been continuously researched and developed. Prior cool roof studies measured their efficiency when applied to buildings or objects. However, research on their application is insufficient. Therefore, this study highlights a broader approach to the effectiveness of the cool roof as a key difference from prior research. To do so, the study focuses on revealing the potential benefits of cool roof with practical applicability, by using aerial images to estimate and describe the construction costs and energy savings associated with cool roof construction in a large urban area. This study proceeded as follows. First, aerial images of eight metropolitan cities in South Korea were collected to construct a dataset for remote sensing. A pre-trained Convolutional Neural Network (CNN) model was employed to detect rooftops in each of the images. The detected rooftops were then clustered according to their surface color and their areas were calculated because the current color determines how much energy can be saved by applying cool roofs. Subsequently, a scenario-based cost-benefit analysis was conducted to estimate the benefits of cool roof application. The results show that cool roofs can reduce cooling energy use, thereby reducing greenhouse gas emissions, and increase urban sustainability.