Synthetic aperture radar (SAR) imagery is invaluable for assessing disaster-induced changes due to its capacity to capture detailed surface information despite varying environmental conditions. However, the scarcity of high-resolution (HR) SAR imagery before disasters presents significant challenges for accurately recognizing changes in damaged buildings, particularly in scenarios requiring pre- and post-disaster image pairs for machine-learning methods that rely on large samples. To address this challenge, our study proposes an innovative solution utilizing simulated SAR imagery generated through ray tracing-based SAR simulation, we generated high-quality predisaster SAR images that closely replicate the scattering properties of authentic SAR imagery using the same sensor orientation as postdisaster image. In this study, to evaluate the feasibility of using simulated SAR images for deep learning-based building damage classification, we investigate three different scenarios: 1) authentic pre- and post-disaster image pairs; 2) simulated predisaster images and authentic postdisaster images; and 3) postdisaster images alone. The methodology was applied in Mashiki, Japan, which was heavily impacted by the 2016 Kumamoto earthquake. Our classification results showed that the simulated predisaster SAR data produced outcomes comparable with those of using authentic image pairs and were clearly superior to the approach that utilized only postevent images. These findings illustrate that simulated SAR imagery is a reliable alternative when authentic predisaster data are unavailable, enabling fast, and accurate damage assessments to support emergency decision-making.

To issue early warnings and rapidly initiate disaster responses after tsunami damage, various tsunami inundation forecast systems have been deployed worldwide. Japan's Cabinet Office operates a forecast system that utilizes supercomputers to perform tsunami propagation and inundation simulation in real time. Although this real-time approach is able to produce significantly more accurate forecasts than the conventional database-driven approach, its wider adoption was hindered because it was specifically developed for vector supercomputers. In this paper, we migrate the simulation code to modern CPUs and GPUs in a minimally invasive manner to reduce the testing and maintenance costs. A directive-based approach is employed to retain the structure of the original code while achieving performance portability, and hardware-specific optimizations including load balance improvement for GPUs are applied. The migrated code runs efficiently on recent CPUs, GPUs and vector processors: a six-hour tsunami simulation using over 47 million cells completes in less than 2.5 minutes on 32 Intel Sapphire Rapids CPUs and 1.5 minutes on 32 NVIDIA H100 GPUs. These results demonstrate that the code enables broader access to accurate tsunami inundation forecasts.

Urban areas in Lima, Peru, grow fast and with almost no control. Furthermore, resources to monitor urban areas are limited in Peru. Thus, the evolution of exposure of urban areas to natural hazards is highly uncertain. In this paper, we report a practical use of deep learning-based land use and land cover mapping to quantify, with unprecedented accuracy, the increment in exposure against floods in the District of Ate, Lima, Peru. We use Google Satellite imagery recorded in 2002 and 2023, generate land use maps, and quantify the increment of urban areas in the floodplain of the Rimac River. Results show an increment of exposure to floods of about 10.3% after 21 years.

The lack of pre-disaster data often poses challenges for reliable building damage predictions in disaster response using remote sensing. To address this limitation, we propose a framework that leverages Synthetic Aperture Radar (SAR) simulators to generate high-resolution simulated SAR images. Assessing the realism of these simulated images is crucial for their reliability in representing authentic SAR data. However, obtaining pre-disaster SAR images for city-scale areas can be challenging.In this study, we utilized Google 3D Tiles and Blender-GIS model to recreate scenes for simulating pre-disaster SAR images. We conducted a comprehensive analysis, evaluating the similarity between simulated and authentic SAR images through similarity assessments. Preliminary results suggest that the SAR simulator successfully produces distinct signatures for different 3D models in simulated SAR imagery. This approach holds promise for use in disaster response scenarios.

Remote sensing and deep learning have been integrated to solve multiple problems, including building damage assessment. With rapid development in both fields, deep learning and remote sensing can play a greater role in damage mapping, specifically in rapid damage assessment, to support emergency response efforts. Deep learning model evaluation is generally based on a statistical split separating training and testing sets of the same data distribution. Although this enables the evaluation of the model performance, this scheme does not disclose the ability of the model to perform in data obtained from different distributions, which is often the case in real-context disaster emergency response. This study evaluates the model generalization in emergency response scenarios. The results show that the current deep learning model has a high performance in in-domain testing yet experiences a drop of up to 53(%) in F1in realistic applications. Future studies should focus on enhancing the model transferability, including using domain adaptation techniques and harnessing multi-modal features.

In recent years, unmanned aerial vehicles (UAVs) have played an increasingly crucial role in supporting disaster emergency response efforts by analyzing aerial images. While current deep-learning models focus on improving accuracy, they often overlook the limited computing resources of UAVs. This study recognizes the imperative for real-time data processing in disaster response scenarios and introduces a lightweight and efficient approach for aerial video understanding. Our methodology identifies redundant portions within the video through policy networks and eliminates this excess information using frame compression techniques. Additionally, we introduced the concept of a station point, which leverages future information in the sequential policy network, thereby enhancing accuracy. To validate our method, we employed the wildfire FLAME dataset. Compared to the baseline, our approach reduces computation costs by more than 10 times while improving accuracy by 3%. Moreover, our method can intelligently select salient frames from the video, refining the dataset. This feature enables sophisticated models to be effectively trained on a smaller dataset, significantly reducing the time spent during the training process.

Deep learning models are generally less able to maintain their performance in out-of-domain testing. Model transferability is crucial, especially when a model needs to be applied to a new dataset, such as in disaster emergency response, where the training samples are scarce. To solve the aforementioned issues, we propose a semi-supervised framework to improve model generalization by utilizing unlabelled samples from the target domain. The framework consists of two main steps: model initialization, which incorporates past events, and iterative fine-tuning. The latter step relies heavily on the pseudo-labels inferred with high confidence from the former step. We tested our framework on the 2024 Noto Peninsula Earthquake. Our framework shows an improvement in the model generalization indicated by higher scores in the tuned model compared to the initial model. The effect is even greater when the local context from the past event is included in the initial learning step. In this case, the score has increased by about 21% from 0.62 to 0.75. The proposed framework offers a promising solution for rapid disaster damage mapping.

After a major disaster, a rapid assessment of building damage is highly required for emergency response and prompt recovery. Remote sensing technologies have been widely applied for building damage mapping. Combining machine-learning algorithms (e.g., deep learning) and satellite images has recently demonstrated success in boosting damage recognition methods. Although previous techniques have shown great success, they primarily adopt supervised settings, often requiring a minimum number of training samples to achieve acceptable accuracy. Moreover, previous methods also are developed for specific target areas, which makes it challenging to apply them to other regions in case of future disasters. This paper presents a novel unsupervised approach for building damage mapping, focusing on collapsed structures, using modern convolutional neural network (CNN) models and high-resolution remote sensing imagery. We apply our mapping framework to revise the building damage following the 2007 Peru-Pisco Earthquake and the recent 2023 Turkey and Syria Earthquakes.

Remote sensing has been used extensively to identify the extent of floods. However, few studies have addressed the estimation of inundation depth, which would provide a deeper understanding of the affected areas. This paper reports a step-by-step application of a novel method to estimate inundation depths during a flood in Mabi town, Okayama Prefecture, Japan 2018. The method is based on the solution of a nonlinear programming problem, in which the flood extent, computed from SAR imagery, is represented as a sparse linear combination of water bodies calculated from a digital elevation model. The results show a good agreement with observations on the field survey and can be implemented in a fully automatic framework.

We show the application of an automatic collection of training samples for the identification of flooded buildings. The method is based on a near real time estimation of the flooded area using in-place sensors and a numerical simulation. Then, microwave remote sensing images are used to improve the accuracy of the extent of the flooded area. The floods produced during the 2018 heavy rainfalls in the town of Mabi is reported as case study. The results are consistent with the flood map provided by a third party.

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake–tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings.

Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the most critical and difficult tasks is accurately assessing the extent and severity of the disaster. This task is especially challenging in areas isolated by the disaster; in such cases, remote sensing information provides the best alternative to tackle this problem. This paper presents a damage mapping framework using remote sensing imagery acquired from previous disasters. The proposed deep learning-based framework is trained to learn features related to building damage using imagery from previous disasters that were collected from different regions around the world. Then, it is tested to recognize damage from a different urban environment.

This paper presents a novel application of remote sensing data and machine learning technologies for damage classification in a real-world cross-domain application. The proposed methodology trains models to learn the building damage characteristics recorded in the 2011 Tohoku Tsunami from multi-sensor and multi-temporal remote sensing images. Then, the trained models are tested in the recent 2018 Sulawesi Tsunami. Additionally, a simulation of high-resolution SAR image was carried to deal with missing data modality. Our initial results show that the ResNet-derived features from optical images acquired after the disaster together with moderate- and high-resolution synthetic aperture radar (SAR) post-event intensity data showed significant accuracy in classifying two levels of tsunami-induced damage, with an average f-score of approximately 0.72. Taking into account that no training data from the 2018 Sulawesi Tsunami was used, our methodology shows excellent potential for future implementation of a rapid response system based on a database of building damage constructed from previous majors disasters.

In this study, a function to estimate tsunami inundation depth using pre- and post-event high-resolution synthetic aperture radar (TerraSAR-X) data was derived and the performance was evaluated. After the tsunami disaster, it is important to identify an extensive impact caused by a tsunami disaster. Tsunami inundation depth is an important index to expect building damage because it has a strong correlation with the amount of building damage. However, it was diffcult to estimate the tsunami inundation depth from satellite image. This study aims at developing a method to estimate tsunami inundation depth by integrating remote sensing technology and tsunami engineering. The method for estimating tsunami inundation depth consists of two steps, 1) Change detection of pre- and post-event TerraSAR-X data that captured affected areas due to the 2011 Tohoku earthquake and tsunami, 2) Estimation of tsunami inundation depth. The new function showed good performance with the correlation coefficient of R = 0.68.

In the 2011 Great East Japan Earthquake and Tsunami Disaster, approximately 23 million tons of debris was estimated over wide areas caused by the tsunami damage. Quantitative estimation of tsunami debris is essential from disaster response point of view. In this study, a novel remote sensing method has been developed for directly measuring the amount of debris in the affected areas and for mapping the results. Specifically, two methods are integrated. The first extracts spatial extent of debris areas using optical sensor data (digital aerial photos and satellite images) obtained immediately after tsunami disaster. The second determines the amount of debris on the ground and quantitatively estimates its amount (volume, weight) by integrating analysis that uses the height of the debris obtained by airborne light detection and ranging (LiDAR).The analysis was conducted in Onagawa town, Miyagi Prefecture, one of the most severely affected by the 2011 event, including the ground truth data acquisition. The horizontal mapping of tsunami debris in Onagawa town was performed through object-based image analysis of aerial and satellite images. Integration of horizontal mapping of tsunami debris and the analysis of digital surface model (DSM) of LiDAR data yields an estimate of the volume of the debris to be used as the observation of debris removal efforts.

Debris estimation is one of the most important initial challenges after a disaster like the Great East Japan Earthquake and Tsunami. Reasonable estimates of the debris must be made available to decision makers as quickly as possible. Classical approaches to obtain this information are far from being optimal, usually relying on manual interpretation of optical imagery. We have developed a novel approach for the estimation of tsunami debris pile heights and volumes for improved emergency response. The method is based on a stereo-synthetic aperture radar (stereo-SAR) approach for very high-resolution airborne polarimetric SAR. An advanced gradient-based optical-flow estimation technique is applied for optimal image coregistration of the low-coherence non-interferometric data. Its suitability to combine multiresolution data allows generating stereo SAR data by combining two different sensors. Based on model-based decomposition of the PolSAR data, only the odd bounce scattering contributions are used to optimize echo time computation. In this paper, we propose the further development of the method by combining multiresolution data from i) air-/spaceborne SAR and spaceborne/spaceborne SAR with various illumination geometries. The proposed technique is validated using in situ data of real tsunami debris taken on a temporary debris management site in the tsunami affected area near Sendai city, Japan. The estimated height error is in the order of 0.7 m RMSE. The good quality of derived pile heights allows estimating debris volume with an RMSE of 2500 m3corresponding to <; 10% of the total debris volume. Advantages of the proposed method are fast computation time, and robust height and volume estimation of debris piles without the need for pre-event data or auxiliary information like DEM, topographic maps or GCPs

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.

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.

The tsunami disasters that occurred in Indonesia, Chile, and Japan have inflicted serious casualties and damaged social infrastructures. Tsunami forecasting systems are thus urgently required worldwide. We have developed a real-time tsunami inundation forecast system that can complete a tsunami inundation and damage forecast for coastal cities at the level of 10-m grid size in less than 20 min. As the tsunami inundation and damage simulation is a vectorizable memory-intensive program, we incorporate NEC’s vector supercomputer SX-ACE. In this paper, we present an overview of our system. In addition, we describe an implementation of the program on SX-ACE and evaluate its performance of SX-ACE in comparison with the cases using an Intel Xeon-based system and the K computer. Then, we clarify that the fulfillment of a real-time tsunami inundation forecast system requires a system with high-performance cores connected to the memory subsystem at a high memory bandwidth such as SX-ACE.

In this study, a practical methodology was presented to map damaged buildings using high resolution synthetic aperture radar (SAR) images and post-event building damage data from the 2013 Super Typhoon Haiyan, in Tacloban city, the Philippines. To detect destroyed structures, we focused on the changes in the radar signal within footprints of buildings between pre- and post-event SAR images. The method was tested using a 1.0 m resolution COSMO-SkyMed SAR images taken over Tacloban city, the Philippines. The method proves, with 73% accuracy in this case, to be suitable for estimating destroyed buildings.

When a tsunami warning is officially announced, as a measure for safeguarding ships from tsunami attacks, it is recommended that large ships in harbors be maneuvered to sheltered areas outside the ports until the tsunami subsides. In this paper, we develop mathematical simulation models to describe the dynamic behavior of a ship. Then, the evacuation maneuvers of a cruise ship and a cargo ship in a tsunami flow are analyzed and characterized. Finally, it is concluded that this kind of simulation could be very great help useful to evaluate the safety of ship evacuation methods against for the tsunami attacks.

In this letter, a new approach is proposed to classify tsunami-induced building damage into multiple classes using pre- and post-event high-resolution radar (TerraSAR-X) data. Buildings affected by the 2011 Tohoku earthquake and tsunami were the focus in developing this method. In synthetic aperture radar (SAR) data, buildings exhibit high backscattering caused by double-bounce reflection and layover. However, if the buildings are completely washed away or structurally destroyed by the tsunami, then this high backscattering might be reduced, and the post-event SAR data will show a lower sigma nought value than the pre-event SAR data. To exploit these relationships, a rapid method for classifying tsunami-induced building damage into multiple classes was developed by analyzing the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage. The method was developed for the affected city of Sendai, Japan, based on the decision tree application of a machine learning algorithm. The results provided an overall accuracy of 67.4% and a kappa statistic of 0.47. To validate its transferability, the method was applied to the town of Watari, and an overall accuracy of 58.7% and a kappa statistic of 0.38 were obtained.

In this study, the extent of the flooded areas by the Super Typhoon Haiyan in the Philippines were extracted using ASTER VNIR images taken over Tacloban city in the Visayas. In order to constraint the affected area, we employed the normalize difference vegetation and water indices (NDVI and NDWI) from the pre- and post-event images. The extension of the flooded area was determined by comparing the index characteristics before and after the event. A phase-based change detection method indices was applied to classify the affected area into three classes according to the changes between the pre- and post-images. Through NDWI the flooded areas were detected despite the moderate resolution of ASTER images. In addition, the phase-based analysis successfully detected level of change within the affected area that may be correlated to the damage observed on field surveys. The results from the phase-based analysis were verified with damage levels obtained through visual damage inspection using high resolution satellite images.

In this study, a semi-automated method to estimate building damage in a tsunami affected area is developed using pre- and post-event high-resolution synthetic aperture radar (TerraSAR-X) data. For development, some coastal areas affected by the 2011 Tohoku earthquake tsunami were focused. The method for estimating building damage consists of three steps, 1) To detect flooded areas by the tsunami, 2) To detect built-up areas, 3) To estimate building damage inside the flooded built-up areas. The previously proposed methods using high-resolution SAR data needs building footprint data for estimating building damage[1]. However, this problem was improved by developing a new method which does not need building footprint data to estimate building damage caused by the tsunami. The developed method was validated on the other test sites and the estimated results showed good consistency with the ground truth data.

A strong typhoon “Haiyan” affected Southeast Asia on November 8, 2013, caused gigantic destruction in the Philippines. In this study, two pre- and one post-event COSMO-SkyMed SCSB data were used to detect the damaged area around Tacloban City, Leyte Island. First, the severe damaged areas were detected according to the difference between the pre- and post-event speckle divergence values. Then the pre- and co-event coherence (NDCI) and correlation coefficient (NDCOI) were calculated from the three temporal data. The relationships between the four building damage levels and NDCI or NDCOI value were obtained by introducing the visual interoperation result. Using this relationship, the possibility of each damage class was estimated in the whole urban area.

Using the aerial photos published by Geospatial Information Authority of Japan (GSI), the authors visually inspected the building damage to identify the structural vulnerability in the tsunami affected area due to the 2011 Tohoku earthquake tsunami. First, the electronic map of buildings and the aerial photos were superimposed on GIS. Then, visual inspection of building damage was conducted in Iwate and Miyagi Prefectures, and in order to identify the structural vulnerability in each municipality, damage probabilities(PD) were calculated by taking a ratio of the number of devastated buildings those were washed away over the number of total buildings exposed by the tsunami. Finally, the relationship between the number of devastated buildings and the number of fatalities were discussed to identify local vulnerability against the tsunami.

The 2011 Tohoku earthquake tsunami totally devastated the communities of Sanriku coast. Because of the destruction of tsunami observation facilities, such as tide gauges, there are difficulties to understand local tsunami characteristics. The author analyzed the videos recorded at two sites to identify the local tsunami inundation characteristics. The video analysis based on 2-D projective transformation and field measurements enables to estimate the tsunami front and flow velocities precisely, e.g. 8 m/s on Sendai plain and 6 m/s at Onagawa town. The results contribute to understand the local tsunami flow characteristics that were not documented by the observation.

The 11 March 2011 Tohoku, Japan earthquake caused gigantic tsunamis and widespread devastations. Various satellites quickly captured the details of affected areas, and were used for emergency response. In this study, high-resolution pre- and post-event TerraSAR-X (TSX) intensity images were used to identify damaged buildings. Since the damaged buildings show changes in backscattering intensity, they can be detected by calculating the difference. A GIS map was introduced to identify individual damaged buildings and investigate their characteristics. According to the side-looking nature of SAR sensors, the buildings' shapes obtained from the GIS map were converted to match their locations in the TSX images. Then washed-away and damaged buildings were extracted using the changed area of SAR intensity within a building's wall and outline. The results were compared with visual interpretation results, and the accuracy of the proposed method was confirmed.

The paper outlines new research findings and hereof generated products in the field of earth observation and modeling technologies to support emergency response measures. Based on the recent earthquake and tsunami disaster in Japan (March 2011) examples will be given for new methodological developments and products to support emergency response strategies more effectively.

The authors developed a method of object-based satellite image analysis using high-resolution post-tsunami satellite image to detect and map tsunami impact. The method is applied to QuickBird 4 band pan-sharpened composite image acquired in Banda Aceh, Indonesia, and the ground objects are classified into six ; vegetation, water, soil, building, road and debris, for mapping the impact of the 2004 Sumatra Andaman earthquake tsunami.

A new tsunami observation system has been developed, which employs the RTK-GPS technique to detect a tsunami before it reaches the coast. After a series of preliminary experimental studies, the operation-oriented experiments were conducted at two offshore sites. These systems succeeded to detect four 10cm tsunamis on 23rd June 2001 Peru earthquake, 26th September 2003 Tokachi earthquake, 5th September 2004 Kii earthquake and 28th February 2010 Chile earthquake. The newly established Muroto GPS buoy system is continuously operating now. The developed GPS buoy system has been adopted as a part of the NOWPHAS by the Ministry of Land, Infrastructure, Transport and Tourism. These GPS buoys successfully recorded the tsunami of the 28th February 2010 Chile earthquake (Mw8.6). These results well substantiate of a GPS buoy to be a powerful tool for early detection of tsunami. On going and future potential development of the system include (1) continuous observing system of ocean bottom crustal deformation using GPS-Acoustic system, (2) observation of tropospheric zenith delay for application to atmospheric research through estimating water vapor content, but its potential capability may not be limited only to these.

The present paper reports a preliminary result of searching tsunami-affected area using recent advances of GIS analysis and remote sensing combined with a numerical modeling of tsunami propagation/inundation and world population database. Applying the method of searching tsunami affected area to the 2009 Samoa earthquake tsunami and the 2010 Chilean earthquake tsunami, the potential tsunami affected area have been detected at some coastal cities/communities. The results are utilized to detecting tsunami impacted area for conducting disaster relief activities.

In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper introduces a likelihood function to estimate severe damage ratio by earthquakes on the basis of dataset from JERS-1/SAR (L-band SAR) images observed the 1995 Kobe earthquake and its detailed ground truth data. The model is applied to JERS-1/SAR images taken over the tsunami affected areas by the 1993 Hokkaido Nansei-oki, Japan earthquake.