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.

Vegetation is a key component of biodiversity and ecosystem stability. The normalized difference vegetation index (NDVI) is widely used to monitor the vegetation growth status. Timely spatiotemporal prediction of NDVI is of great significance to agriculture and the ecological environment. Traditional deep learning models neglect the local spatial relationships and the effect of spatial autocorrelation in vegetation, failing to capture the neighboring contributions to the forecast target. In this study, a geographically weighted convolutional long short-term memory (GWConvLSTM) model is coupled with the spatial weight and the convolutional long short-term memory (ConvLSTM) model by the Hadamard product is proposed to predict NDVI. In the GWConvLSTM model, a spatiotemporal memory flow with the ConvLSTM unit is designed to calculate the spatial weight of each step, which is iteratively updated with the training of the model. We use the proposed GWConvLSTM model to forecast the monthly NDVI of Huangpi District and the experiment results indicate that the developed GWConvLSTM model outperforms five other baseline models. The predictive coefficient of determination ($R^{2}$) reached up to 0.8427, 0.8209, and 0.8149 for one-, three-, and five-month lead times, respectively. The mean absolute error (MAE) decreased by 14.26%, 10.04%, and 16.75% for GWConvLSTM versus the baseline ConvLSTM model in one-, three-, and five-month NDVI predictions, respectively. Therefore, the developed GWConvLSTM model could be an effective geospatial deep learning model for spatiotemporal NDVI prediction at region scales.

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.