To protect the population from respiratory diseases and to prevent the damages due to air pollution, the main cause of air pollution should be identified. This research assessed the relationship between the airborne particulate concentrations (PM₁₀) and the urban expansion in Daegu City in South Korea from 2007 to 2017 using multi-temporal spatial datasets (Landsat images, measured PM₁₀ data) and the machine learning technique in the following steps. First, the expanded urban areas were detected from the multiple Landsat images using support vector machine (SVM), a widely used machine learning technique. Next, the annual PM₁₀ concentrations were calculated using the long-term measured PM₁₀ data. Finally, the degrees of increase of the expanded urban areas and of the PM₁₀ concentrations in Daegu from 2007 to 2017 were calculated by counting the pixels representing the expanded urban areas and computing variation of the annual PM₁₀ concentrations, respectively. The experiment results showed that there is a minimal or even no relationship at all between the urban expansion and the PM₁₀ concentrations because the urban areas expanded by 55.27 km² but the annual PM₁₀ concentrations decreased by 17.37 μg/m³ in Daegu from 2007 to 2017.

Choung, Y.-J. and Jung, D. 2021. Comparison of machine and deep learning methods for mapping sea farms using high-resolution satellite image. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 420–423. Coconut Creek (Florida), ISSN 0749–0208. Previous research had shown that the supervised machine learning approach performed better than unsupervised machine learning for mapping sea farms using a high-resolution satellite image. The present work compares a support vector machine (SVM), which represents the supervised machine learning approach, and a deep neural network (DNN), which represents the deep learning approach, for mapping sea farms using KOMPSAT-3 satellite images acquired in the South Sea of South Korea. First, coastal maps were generated from the image source given by SVM and DNN. Next, the above-water and underwater farms were detected separately from both the maps based on the minimum and maximum thresholds. Finally, the detection accuracy of both the above-water and underwater farms from both coastal maps was assessed. Statistical results showed that deep learning (DNN) provided better performance than machine learning (SVM) for detecting above-water farms from the given high-resolution satellite image, while both DNN and SVM yielded the same performance for underwater farms. However, a few errors occurred in the detection because of the limitations of the pixel-based classification approaches. In future research, the deep learning algorithm combined with object-based classification, such as the convolutional neural network, can be used to detect sea farms from the given high-resolution image more accurately.