Recently, Japan has been struck by extremely heavy rains and serious floods. To plan for disaster response, it is needed to consider various information such as precipitation, water level of rivers, weather forecasts, and so on. However, it requires high skill to integrate this information to utilize. Our study aims to provide the decision support information for both the national and the local governments by dynamic risk analysis to enforce disaster resilience. To demonstrate the effectiveness of disaster dynamics analysis, we have developed the “Dynamic Decision Support System for Disaster Response (DDS4D)”. DDS4D synthesizes natural observation data, social observation data, and geospatial data to provide decision support information that fits decision maker's the situational awareness in real time. Verifying several information products generated by DDS4D in the actual flood in Japan, we confirmed that they could support decision making of government.

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable.

Title: Disaster Information Sharing Technology Among Heterogeneous Information Systems Through SIP4D-ZIP Framework | Keywords: disaster information, information sharing, standard, disaster response | Author: Makoto Hanashima and Yuichiro Usuda

In a disaster situation, local and municipal governments need to distribute relief supplies and provide administrative support to evacuees.Although people are supposed to evacuate to evacuation shelters designated by local governments, some people take refuge at non-designated facilities, called non-designated evacuation shelters , due to unavoidable circumstances such as damage on the access routes to designated evacuation shelters. Upon occurrence of a disaster, therefore, it is necessary for the local governments to quickly find the locations of non-designated evacuation shelters. In this article, we propose a method to detect non-designated evacuation shelters based on autoencoder (AE)-based anomaly detection using real-time population dynamics generated from operation data of cellular phone networks. We assume that reconstruction errors of an AE model include both the errors due to characteristic differences between locations and the errors due to anomalies in population dynamics. Thus, we propose to use the ratio of the reconstruction error before and after the earthquake to determine the threshold of anomaly detection. We evaluate the performance of the proposed method on data from three actual earthquakes in Japan. The evaluation results show that our reconstruction error based approach can achieve better accuracy for the actual disaster data compared to a baseline method that exploits statistical anomaly detection.

It is important for local governments to grasp the evacuation situation upon the occurrence of a large-scale disaster because administrative support can be provided, such as opening temporary accommodation facilities or distributing relief supplies. Early identification of locations where population transitions differ from normal conditions will help local governments to identify areas for disaster response. In this paper, we propose a simple yet effective anomaly detection method for population transition using an autoencoder that leverages periodic residual components using real-time population dynamics generated from operation data of cellular phone networks. We evaluated the effectiveness of our proposed method using data from three real earthquakes in Japan. The results demonstrated that our approach, which leverages periodic residual components, outperforms a baseline method that directly uses population transition. Moreover, the experimental results showed that the standard deviation of recall is smaller than that of the baseline method, indicating that utilizing the residual component has a stabilizing effect on accuracy.