Investigating the relationship between air temperature and the intensity of urban development using on-site measurement, satellite imagery and machine learning

•Air temperature can be predicted using features extracted from satellite imagery.•10% increase in building coverage may cause a rise of 0.28°C in air temperature.•10% increase in vegetation coverage may cause good cooling effect within an area.•Well-trained machine learning models can help understand the general urban climate.•Well-trained neural networks can help assess and map air temperature distribution. Given the seriousness of the urban heat problem, the relationship between urbanization and air temperature has become a critical concern worldwide. In this study, common urban planning indicators, including the building coverage ratio (BCR), floor area ratio (FAR), and fractional vegetation cover (FVC), were extracted from satellite images to determine the intensity of urban development. On-site measurements and machine learning (ML) were used to observe and analyze the relationship between the intensity of urban development and air temperature. From the on-site measurement results, the air temperature in downtown Taipei decreased by an average of approximately 0.32°C with every 10% increase in the FVC. However, it increased by an average of approximately 0.28°C and 0.03°C with every 10% increase in the BCR and FAR, respectively. The results obtained from the ML models demonstrated the same trend, with minor differences from the on-site measurement results, which were regarded as reasonable and acceptable. In this study, a more convenient method was proposed to extract urban planning indicators, describe the intensity of urban development within an area, and help estimate air temperature in areas without measuring instruments. The relationship determined herein may aid in the decision-making process of the balance of urbanization and vegetation.