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In this study, a polyethyleneimine (PEI)-functionalized carbon nanotube (CNT) sensor was fabricated for carbon dioxide detection at room temperature. Uniform CNT thin films prepared using a filtration method were used as resistive networks. PEI, which contains amino groups, can effectively react with CO2 gas by forming carbamates at room temperatures. The morphology of the sensor was observed, and the properties were analyzed by scanning electron microscope (SEM), Raman spectroscopy, and fourier transform infrared (FT-IR) spectroscopy. When exposed to CO2 gas, the fabricated sensor exhibited better sensitivity than the pristine CNT sensor at room temperature. Both the repeatability and selectivity of the sensor were studied.

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Recent studies have enhanced the mapping performance of the local climate zone (LCZ), a standard framework for evaluating urban form and function for urban heat island research, through remote sensing (RS) images and deep learning classifiers such as convolutional neural networks (CNNs). The accuracy in the urban-type LCZ (LCZ1-10), however, remains relatively low because RS data cannot provide vertical or horizontal building components in detail. Geographic information system (GIS)-based building datasets can be used as primary sources in LCZ classification, but there is a limit to using them as input data for CNN due to their incompleteness. This study proposes novel methods to classify LCZ using Sentinel 2 images and incomplete building data based on a CNN classifier. We designed three schemes (S1, S2, and a scheme fusion; SF) for mapping 50 m LCZs in two megacities: Berlin and Seoul. S1 used only RS images, and S2 used RS and building components such as area and height (or the number of stories). SF combined two schemes (S1 and S2) based on three conditions, mainly focusing on the confidence level of the CNN classifier. When compared to S1, the overall accuracies for all LCZ classes (OA) and the urban-type LCZ (OAurb) of SF increased by about 4% and 7–9%, respectively, for the two study areas. This study shows that SF can compensate for the imperfections in the building data, which causes misclassifications in S2. The suggested approach can be excellent guidance to produce a high accuracy LCZ map for cities where building databases can be obtained, even if they are incomplete.