Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.

Amidst the intensifying extreme rainfall patterns due to climate change, global early warning systems for mass movements (e.g. landslides, avalanches) need to provide not only the coarsely aggregated danger reports, but also the necessary fine details to understand its potential implications on critical infrastructures such as transportation systems. In this study, we introduce a novel ‘intergraph’ method that enhances the exposure information using a graph-based machine learning implementation on the hydrological and geological characteristics of mass movements and the underlying connectivity of settlement-transportation systems. Demonstrating the entire country of Norway and the 2020 Gjerdrum quick clay incident as a case study, we integrated the assessment of both direct and indirect exposure information of settlement-transportation systems and their daily 1 km-by-1 km susceptibility map, which were derived from the 68 934 mass movement incidents since 1957 and the connectivity information of 4778 settlements and 257 000 km road networks. Our findings achieved 86.25% accuracy, providing a distribution of improved susceptibility estimates and identifying critical settlements in near-real-time. By interacting the graphical representations of the shared causal drivers of susceptibility and the settlement-transportation system connectivity, our study extends our understanding of the exposure of multiple interacting settlements with a high granularity degree in a unified approach.

The electrical conductivity of simulated cement paste matrixes with water-to-cement ratios of 0·50 and 2·0% amorphous nanosilica, synthesized from rice hull ash, has been related to the effect of hydration stages on the ions dissociated in the capillary pore solution of the cement paste mesostructure. The increase in ions, as the hydration proceeded, has also been implied by the increasing trend of the conductivity spectra obtained from the complex impedance spectroscopy in the frequency region from 100 kHz to 20 MHz. In the 10⁵–10⁷ Hz spectra, the conductivity was also found to exhibit a power-law behavior. To consider the effect of capillary pores, a comparable set of cement pastes with a similar macrostructure has been established using a modified method of simple grinding and pelletizing with a similar techniques. From the results of the Kramers–Kronig validation test, the impedance data have been found to exhibit a causal, linear and stable behavior in the high-frequency region from 6 to 20 MHz.

As the world marked the midterm of the Sendai Framework for Disaster Risk Reduction 2015-2030, many countries are still struggling to monitor their climate and disaster risk because of the expensive large-scale survey of the distribution of exposure and physical vulnerability and, hence, are not on track in reducing risks amidst the intensifying effects of climate change. We present an ongoing effort in mapping this vital information using machine learning and time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 Harmonized MSI. We introduce the development of "OpenSendaiBench" consisting of 47 countries wherein most are least developed (LDCs), trained ResNet-50 deep learning models, and demonstrated the region of Dhaka, Bangladesh by mapping the distribution of its informal constructions. As a pioneering effort in auditing global disaster risk over time, this paper aims to advance the area of large-scale risk quantification in informing our collective long-term efforts in reducing climate and disaster risk.

Tracking spatiotemporal changes in disaster risk is essential for monitoring progress toward the UN Sendai Framework for Disaster Risk Reduction (SFDRR) 2015-2030. Despite rapid advances in information technology and big data, our collective progress remains insufficient. Existing SFDRR indicators offer simple and globally comparable metrics at the national level but rely heavily on short-term trends and inadequately capture the probabilistic nature of disaster risk. While large-scale modeling of hazard and building exposure has already advanced significantly through Earth observation and data-driven methods, progress still lags in modeling another equally important yet challenging element of the risk equation: physical vulnerability. Therefore, we develop a data-driven probabilistic approach to model the regional dynamics of building exposure and physical vulnerability over time. Our work combines recent advances in graph deep learning, state-space modeling, and variational inference, leveraging time-series satellite-derived products with existing expert belief systems. We present METEOR 2.5D, an open geospatial dataset of the spatiotemporal evolution of physical vulnerability in UN-recognized Least Developed Countries (as of 2020) at five-year intervals, 1975-2030. We integrate rasterized temporal exposure datasets, such as DLR World Settlement Footprint Evolution and Global Human Settlement Layer multitemporal products, with the existing static METEOR dataset as prior information to generate dynamic maps with a five-fold improvement in spatial resolution (i.e., from 450-meter to 90-meter scale). By addressing critical gaps in modeling physical vulnerability at large scales, our work enhances the understanding and auditing of our global disaster risk, both now and beyond 2030. The METEOR 2.5D dataset is publicly available in two parts: https://doi.org/pzq4 and https://doi.org/pzrd.

Sea ice plays a vital role in Earth’s human-climate system. It regulates the Earth’s overall energy balance by seasonally increasing surface albedo and reflecting solar radiation; it governs thermodynamic exchanges between the ocean and atmosphere and thereby impacts mid-latitude weather patterns; it buttresses key continental ice shelves in Greenland and Antarctica; it provides an ecosystem in which land, marine, and airborne species thrive; it enables the livelihoods of indigenous populations across the Arctic; it poses a major obstacle to global shipping logistics; and it serves as a key indicator of climate change given the sensitivity of the polar regions to anthropogenically-induced warming. Regular and automated monitoring of sea ice concentration and type may therefore prove valuable to a broad and diverse set of parties. Conventional approaches in sea ice monitoring involve the use of remotely sensed microwave radiometer data with low resolution of 6-25 km and high instrumental sensitivities to environmental factors such as atmospheric water vapour, near-surface brightness temperature, and wind-induced surface roughening. Dual-polarity synthetic aperture radar (SAR) imagery offers a higher resolution alternative, which can also distinguish between sea ice and open water year-round independent of weather conditions. However, manual interpretation of such imagery is time-consuming. In this work, we develop a deep learning system to automatically generate high-resolution maps of sea ice concentration and type using 40m-resolution SAR imagery obtained from the Sentinel-1 mission between 2017 and 2021. Focusing on the East Weddell Sea, a region where compacted sea ice is renowned for inhibiting ship navigation and an active area of iceberg calving, we train the system against reference sea ice charts produced through manual interpretation by experts. We identify strengths and weaknesses of the system and discuss implications for future research at the intersection of machine learning and polar science.