Application of Generative Adversarial Network: GAN to disaster damage monitoring

In recent years, natural disasters have caused serious damage. In particular, landslides caused by earthquakes are damaging. However, it is difficult to predict when and where natural disasters will occur. Therefore, this study was conducted on early detection of landslides. SAR (Synthetic Aperture Radar) is a remote sensing technology. It uses microwaves and can observe day and night in all weather conditions. But this SAR data is a grayscale image, which is difficult to analyze without specialized knowledge. Therefore, we decided to use machine learning to detect changes in disasters that appear in SAR data. There are two machine learning models called pix2pix and pix2pixHD for image transformation. The objective of this study is to detect changes of surface by transforming pseudo-optical images from SAR data using machine learning. Two machine learning models were used for training, with test images and actual disaster data input. Simple terrain, such as forests only, was highly accurate, but complex terrain was difficult to generate. About actual disaster data, something like disaster-induced changes appeared in the converted images. However, we found it difficult to distinguish bare area from grassland in the output images. In the future, it is necessary to consider the combination of data to be used for learning.