Estimating the possibility of thermal stress with computer vision and neural networks based on Local Climate Zone and terrain.

Urban Heat Island (UHI) is an evident phenomenon in Taiwan, especially the capital Taipei city, owing to the highly developed with numerous high-rise buildings and the basin terrain. Due to the problem above, a method to estimate the thermal stress possibility based on the urban pattern is necessary. In recent years, machine learning and deep learning developed quickly and achieved in many fields like image recognition, machine translation, and predicting environmental characteristics. With the excellent development of machine learning and deep learning, a novel method for estimating the thermal stress possibility was presented in this work. In this study, the average temperature distribution in Taipei at 1 pm in August 2021 was calculated based on the information from measurement points of the high-density street-level air temperature observation network (HiSAN) and the Central Weather Bureau (CWB). And the thermal condition was classified into three classes: the low, medium, and high possibility of thermal stress based on the median, upper quartile, and top 90% of the data. Simultaneously, Taipei city was classified with Local Climate Zone (LCZ) based on the satellite image from Landsat 8. And this study also collected the terrain data to correct the effect of altitude on temperature. The LCZ map and terrain were resampled into 100m spatial resolution for the subsequent work. Artificial Neural Network (ANN) and Deep Neural Network (DNN) were used and compared this work’s performances for estimating the thermal stress possibilities in Taipei city. The LCZ map and terrain data were reshaped as the images with 64 pixels to quantize pattern features within the areas of 800m² and inputted into the models for training and predicting. Both ANN and DNN models have the same learning rate and training epochs which were 0.001 and 500 epochs. After the training processes of the models, ANN and DNN models were able to estimate the possibility of thermal stress for each area by LCZ map and terrain data. The results of ANN and DNN models proved the feasibility of using algorithms of machine learning and computer vision to assess microclimate conditions. Also, the novel method presented in this work can help estimate the thermal stress possible for the areas without measurement points.