Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Feature

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the Chiba prefecture in Japan, whose damages included with electric provision stop because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took 18 days for recovery longer than past ones. Immediate responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. We demonstrate case studies applied to aerial photographs recorded at the Chiba prefecture after typhoon disaster. (233 words)