Damage mapping after the 2017 Puebla Earthquake in Mexico using high-resolution Alos2 Palsar2 data

On September 19, 2017, the Mw7.1 Puebla Earthquake caused significant destruction in several cities in central Mexico. In this paper, two pre- and one post-event ALOS2-PALSAR2 data were used to detect the damaged area around Izucar de Matamoros town in Mexico. First, we identify the built-up areas using pre-event data. Second, we evaluate the earthquake-induced damage areas using an RGB color-coded image constructed from the pre- and co-event coherence images. Our analysis showed that the green and red bands display a great potential to discriminate the damaged areas.Accepted manuscrip

A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.This work was supported in part by JST CREST, Japan, under Grant JPMJCR1411 and in part by the China Scholarship Council. (JPMJCR1411 - JST CREST, Japan; China Scholarship Council

Fusion of Real-time Tsunami Simulation and Remote Sensing for Mapping the Impact of Tsunami Disaster

Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we establish a method of real-time tsunami inundation forecasting, damage estimation and mapping to enhance disaster response. Right after a major (near field) earthquake is triggered, we perform a real-time tsunami inundation forecasting with use of high-performance computing platform. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data and the numbers of potential death and damaged structures by applying tsunami fragility curve. After the potential tsunami-affected areas are estimated, the analysis gets focused and moves on to the "detection" phase using remote sensing. Recent advances of remote sensing technologies expand capabilities of detecting spatial extent of tsunami affected area and structural damage. Especially, a semi-automated method to estimate building damage in tsunami-affected areas is developed using optical sensor data and a set of pre-and post-event high-resolution SAR (Synthetic Aperture Radar) data. The method is verified through the case studies in the 2011 Tohoku and other potential tsunami scenarios, and the prototype system development is now underway in Kochi prefecture, one of at-risk coastal city against Nankai trough earthquake. In the trial operation, we verify the capability of the method as a new tsunami early warning and response system for stakeholders and responders

Exploiting Resolution-based Representations for MaxSAT Solving

Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver in order to find an optimal solution. In particular, several algorithms take advantage of the ability of SAT solvers to identify unsatisfiable subformulas. Usually, these MaxSAT algorithms perform better when small unsatisfiable subformulas are found early. However, this is not the case in many problem instances, since the whole formula is given to the SAT solver in each call. In this paper, we propose to partition the MaxSAT formula using a resolution-based graph representation. Partitions are then iteratively joined by using a proximity measure extracted from the graph representation of the formula. The algorithm ends when only one partition remains and the optimal solution is found. Experimental results show that this new approach further enhances a state of the art MaxSAT solver to optimally solve a larger set of industrial problem instances

A proposed methodology for deriving tsunami fragility functions for buildings using optimum intensity measures

Tsunami fragility curves are statistical models which form a key component of tsunami risk models, as they provide a probabilistic link between a tsunami intensity measure (TIM) and building damage. Existing studies apply different TIMs (e.g. depth, velocity, force etc.) with conflicting recommendations of which to use. This paper presents a rigorous methodology using advanced statistical methods for the selection of the optimal TIM for fragility function derivation for any given dataset. This methodology is demonstrated using a unique, detailed, disaggregated damage dataset from the 2011 Great East Japan earthquake and tsunami (total 67,125 buildings), identifying the optimum TIM for describing observed damage for the case study locations. This paper first presents the proposed methodology, which is broken into three steps: (1) exploratory analysis, (2) statistical model selection and trend analysis and (3) comparison and selection of TIMs. The case study dataset is then presented, and the methodology is then applied to this dataset. In Step 1, exploratory analysis on the case study dataset suggests that fragility curves should be constructed for the sub-categories of engineered (RC and steel) and non-engineered (wood and masonry) construction materials. It is shown that the exclusion of buildings of unknown construction material (common practice in existing studies) may introduce bias in the results; hence, these buildings are estimated as engineered or non-engineered through use of multiple imputation (MI) techniques. In Step 2, a sensitivity analysis of several statistical methods for fragility curve derivation is conducted in order to select multiple statistical models with which to conduct further exploratory analysis and the TIM comparison (to draw conclusions which are non-model-specific). Methods of data aggregation and ordinary least squares parameter estimation (both used in existing studies) are rejected as they are quantitatively shown to reduce fragility curve accuracy and increase uncertainty. Partially ordered probit models and generalised additive models (GAMs) are selected for the TIM comparison of Step 3. In Step 3, fragility curves are then constructed for a number of TIMs, obtained from numerical simulation of the tsunami inundation of the 2011 GEJE. These fragility curves are compared using K-fold cross-validation (KFCV), and it is found that for the case study dataset a force-based measure that considers different flow regimes (indicated by Froude number) proves the most efficient TIM. It is recommended that the methodology proposed in this paper be applied for defining future fragility functions based on optimum TIMs. With the introduction of several concepts novel to the field of fragility assessment (MI, GAMs, KFCV for model optimisation and comparison), this study has significant implications for the future generation of empirical and analytical fragility functions

Generalized Totalizer Encoding for Pseudo-Boolean Constraints

Pseudo-Boolean constraints, also known as 0-1 Integer Linear Constraints, are used to model many real-world problems. A common approach to solve these constraints is to encode them into a SAT formula. The runtime of the SAT solver on such formula is sensitive to the manner in which the given pseudo-Boolean constraints are encoded. In this paper, we propose generalized Totalizer encoding (GTE), which is an arc-consistency preserving extension of the Totalizer encoding to pseudo-Boolean constraints. Unlike some other encodings, the number of auxiliary variables required for GTE does not depend on the magnitudes of the coefficients. Instead, it depends on the number of distinct combinations of these coefficients. We show the superiority of GTE with respect to other encodings when large pseudo-Boolean constraints have low number of distinct coefficients. Our experimental results also show that GTE remains competitive even when the pseudo-Boolean constraints do not have this characteristic.Comment: 10 pages, 2 figures, 2 tables. To be published in 21st International Conference on Principles and Practice of Constraint Programming 201