It is more than a decade that the Italian Space Agency (ASI) and the Japan Aerospace Exploration Agency (JAXA) are cooperating on the topic of disaster management, by sharing their expertise and satellite assets for the operational use of X-band and L-band SAR data. Over the years, mutual COSMO-SkyMed and ALOS-2 archives were created over Italy and Japan, ad hoc acquisitions were made in response to emergency requests, and data were exchanged to support joint SAR research activities related to Disaster Risk Management. The present paper provides the current status of the cooperation in light of the most recent emergencies, as well as the near future perspectives.

Data augmentation is one of the most effective ways to stabilize learning by improving the generalization of machine-learning models. In recent years, automatic data augmentation methods, such as AutoAugment or Fast AutoAugment have been attracting attention; and these methods improved the results of image classification and object detection tasks. However, several problems remain. Most notably, a larger training dataset requires higher computational costs. When searching with a small dataset in an attempt to determine the data augmentation approach, the true data space and sampling data space do not fully correspond with each other, thereby causing the generalization performance to deteriorate. Moreover, in the existing automatic augmentation methods, the search phase is often dominated by an exceptional sub-policy, which results in a loss of diversity of operations. In this study, we solved these problems by introducing evolutionary computation to previous methods. As mentioned earlier, maintaining diversity is essential. Therefore, we adopted the thermodynamical genetic algorithm (TDGA), which can control the population diversity with a specific genetic operator, known as the thermodynamical selection rule. To confirm the effectiveness of the proposed method, computational experiments were conducted using two benchmark datasets, CIFAR-10 and SVHN, as examples. The experimental results show that the proposed method can obtain various useful augmentation sub-policies for the problems while reducing the computational cost.