Accurate damage estimation after earthquakes is crucial for effective post-disaster response and recovery. However, earthquakes often trigger various additional hazards, such as landslides and liquefaction, making accurate building damage estimation even more challenging. To date, despite significant research efforts, automated, accurate building-specific damage estimation has not been achieved. Our study tackles this challenge. We integrate multi-sourced global building footprints and InSAR coherence-based Change Detection Maps (CDMs) generated by the U.S. Geological Survey (USGS) within a variational causal Bayesian network, providing intricate maps of landslides, liquefaction, and building damage. Our key innovations include: 1) a novel masking strategy for the CDMs, derived from low pre-event mean coherence value and high pre-event coherence standard deviation to eliminate noisy signals in InSAR products induced by irrelevant noise sources (steep slopes, soil moisture and vegetation change, open water, etc.), and 2) variational inference to differentiate potential causes of the changes in InSAR coherence signals, specifically landslides, liquefaction, building damage, and non-hazard changes. Our strategy is critical for enhancing the accuracy of building damage and ground failure assessments, as noise from environmental or human-induced changes can obscure true damage signals. We provide reliable damage identification with attribution to specific causes by focusing on accurate building footprints and improving regional ground failure predictions using the 2023 M6.8 Morocco earthquake to validate our methodology. Our approach enables thorough damage analysis across numerous buildings, with the potential for significantly aiding disaster management and marking a substantial advancement of post-earthquake building damage assessment methods.

Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.

As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation.