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.

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience.

Monitoring wastewater quality at the inlet of a wastewater treatment plant provides an overview of the health of the community connected to the respective wastewater treatment plant. Wastewater monitoring has been increasingly recognized as a valid tool in pandemic risk management, especially during the coronavirus disease 2019 (COVID-19) crisis. However, the results of wastewater monitoring are not yet fully utilized in the decision-making process of public governance across most of the Asia-Pacific region. This study explored the potential of geographic information systems to map the results of wastewater monitoring and provide a common operating picture for swift and informed decision-making, especially in times of disasters. By creating an online dashboard tool, we found that an additional merit of mapping wastewater monitoring is that it allows overlaying various other information, such as evacuation shelters, onto the same map. As the service areas of wastewater treatment plants, or sewersheds, are not necessarily aligned with geopolitical boundaries, mapping provides opportunities for re-calculating wastewater monitoring results at the city or district levels. The potential for recalculation is also applicable to overlayed information, indicating that information from wastewater monitoring may be used to assess disaster preparedness or multi-hazard risks. The dashboard prototype was discussed with government officials and feedback was obtained. We aim to further develop the findings so that the information from wastewater monitoring can be shared on a platform, such as the shared information platform for disaster management, in the future for effective risk governance.

In the event of a disaster, the occurrence of isolated settlements necessitates prompt responses, including rescue operations, medical transport, and the delivery of essential supplies. However, it is often challenging to quickly identify which areas are isolated. This study developed a method for estimating isolated settlements during earthquake disasters using multi-sensing data. An accuracy evaluation based on data from the 2024 Noto Peninsula earthquake revealed an overlook rate of approximately 60% and a mistaken estimation rate of approximately 20%. By incorporating actual road traffic data, the estimation was refined to extract settlements at high risk of isolation. Moreover, the method successfully identified isolated settlements that were not reported in official damage reports, indicating relatively high estimation accuracy. This capability is expected to assist disaster management headquarters in identifying priority areas for emergency response. Because the data used in the proposed method can be obtained during actual disaster events, the estimation process can be initiated promptly across Japan immediately after an earthquake, thereby enabling the timely provision of valuable information for disaster response. This analysis presents the necessary data and computational approaches for improving estimation accuracy and supporting practical implementation in disaster management. In the future, advancements in various sensing technologies and the development of data-sharing frameworks are expected to facilitate even more accurate estimations.