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On 29 December 2020, an earthquake with a magnitude of M 6.4 hit the central part of Croatia. The earthquake resulted in casualties and damaged buildings in the town of Petrinja (~6 km away from the epicenter) and surrounding areas. This study aims to characterize ground displacement and to estimate the location of damaged areas following the Petrinja earthquake using six synthetic aperture radar (SAR) images (C-band) acquired from both ascending and descending orbits of the Sentinel-1 mission. Phase information from both the ascending (Sentinel-1A) and descending (Sentinel-1B) datasets, acquired from SAR interferometry (InSAR), is used for estimation of ground displacement. For damage mapping, we use histogram information along with the RGB method to visualize the affected areas. In sparsely damaged areas, we also propose a method based on multivariate alteration detection (MAD) and naive Bayes (NB), in which pre-seismic and co-seismic coherence maps and geocoded intensity maps are the main independent variables, together with elevation and displacement maps. For training, approximately 70% of the data are employed and the rest of the data are used for validation. The results show that, despite the limitations of C-band SAR images in densely vegetated areas, the overall accuracy of MAD+NB is ~68% compared with the results from the Copernicus Emergency Management Service (CEMS).
Sadra Karimzadeh; Masashi Matsuoka. A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia. Remote Sensing 2021, 13, 2267 .
AMA StyleSadra Karimzadeh, Masashi Matsuoka. A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia. Remote Sensing. 2021; 13 (12):2267.
Chicago/Turabian StyleSadra Karimzadeh; Masashi Matsuoka. 2021. "A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia." Remote Sensing 13, no. 12: 2267.
In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively.
Sadra Karimzadeh; Masashi Matsuoka. Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing. Sensors 2021, 21, 2251 .
AMA StyleSadra Karimzadeh, Masashi Matsuoka. Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing. Sensors. 2021; 21 (6):2251.
Chicago/Turabian StyleSadra Karimzadeh; Masashi Matsuoka. 2021. "Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing." Sensors 21, no. 6: 2251.
Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%.
Mahdi Hasanlou; Reza Shah-Hosseini; Seyd Seydi; Sadra Karimzadeh; Masashi Matsuoka. Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery. Remote Sensing 2021, 13, 1195 .
AMA StyleMahdi Hasanlou, Reza Shah-Hosseini, Seyd Seydi, Sadra Karimzadeh, Masashi Matsuoka. Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery. Remote Sensing. 2021; 13 (6):1195.
Chicago/Turabian StyleMahdi Hasanlou; Reza Shah-Hosseini; Seyd Seydi; Sadra Karimzadeh; Masashi Matsuoka. 2021. "Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery." Remote Sensing 13, no. 6: 1195.
To derive surface displacement, interferometric stacking with synthetic aperture radar (SAR) data is commonly used, and this technique is now in the implementation phase in the real world. Persistent scatterer interferometry (PSI) is one of the most universal approaches among in- terferometric stacking techniques, and non-linear non-parametric PSI (NN-PSI) was proposed to overcome the drawbacks of PSI approaches. The estimation of the non-linear displacements was successfully conducted using NN-PSI. However, the estimation of NN-PSI is not always stable with certain displacements because wider range of the velocity spectrum is used in NN-PSI than the conventional approaches; therefore, a calculation procedure and parameter optimization are needed to consider. In this paper, optimized parameters and procedures of NN-PSI are proposed, and real data processing with Sentinel-1 in the Kanto region in Japan was conducted. We confirmed that the displacement estimation was comparable to the measurement of the permanent global positioning system (GPS) stations, and the root mean square error between the GPS measurement and NN-PSI estimation was less than 3 mm in two years. The displacement over 2π ambiguity, which the conventional PSI approach wrongly reconstructed, was also quantitatively validated and successfully estimated by NN-PSI. As a result of the real data processing, periodical displacements were also reconstructed through NN-PSI. We concluded that the NN-PSI approach with the proposed parameters and method enabled the estimation of several types of surface displacements that conventional PSI approaches could not reconstruct.
Fumitaka Ogushi; Masashi Matsuoka; Marco Defilippi; Paolo Pasquali. Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring. Sensors 2021, 21, 1004 .
AMA StyleFumitaka Ogushi, Masashi Matsuoka, Marco Defilippi, Paolo Pasquali. Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring. Sensors. 2021; 21 (3):1004.
Chicago/Turabian StyleFumitaka Ogushi; Masashi Matsuoka; Marco Defilippi; Paolo Pasquali. 2021. "Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring." Sensors 21, no. 3: 1004.
The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%.
Masoud Hajeb; Sadra Karimzadeh; Masashi Matsuoka. SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan. Applied Sciences 2020, 10, 8932 .
AMA StyleMasoud Hajeb, Sadra Karimzadeh, Masashi Matsuoka. SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan. Applied Sciences. 2020; 10 (24):8932.
Chicago/Turabian StyleMasoud Hajeb; Sadra Karimzadeh; Masashi Matsuoka. 2020. "SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan." Applied Sciences 10, no. 24: 8932.
Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as depth information cannot be obtained, it is difficult to detect collapsed buildings when their roofs are not heavily damaged. Airborne LiDAR can efficiently obtain the 3D geometries of buildings (in the form of point clouds) and thus has greater potential to detect various collapsed buildings. However, there have been few previous studies on deep learning-based damage detection using point cloud data, due to a lack of large-scale datasets. Therefore, in this paper, we aim to develop a dataset tailored to point cloud-based building damage detection, in order to investigate the potential of point cloud data in collapsed building detection. Two types of building data are created: building roof and building patch, which contains the building and its surroundings. Comprehensive experiments are conducted under various data availability scenarios (pre–post-building patch, post-building roof, and post-building patch) with varying reference data. The pre–post scenario tries to detect damage using pre-event and post-event data, whereas post-building patch and roof only use post-event data. Damage detection is implemented using both basic and modern 3D point cloud-based deep learning algorithms. To adapt a single-input network, which can only accept one building’s data for a prediction, to the pre–post (double-input) scenario, a general extension framework is proposed. Moreover, a simple visual explanation method is proposed, in order to conduct sensitivity analyses for validating the reliability of model decisions under the post-only scenario. Finally, the generalization ability of the proposed approach is tested using buildings with different architectural styles acquired by a distinct sensor. The results show that point cloud-based methods can achieve high accuracy and are robust under training data reduction. The sensitivity analysis reveals that the trained models are able to locate roof deformations precisely, but have difficulty recognizing global damage, such as that relating to the roof inclination. Additionally, it is revealed that the model decisions are overly dependent on debris-like objects when surroundings information is available, which leads to misclassifications. By training on the developed dataset, the model can achieve moderate accuracy on another dataset with different architectural styles without additional training.
Haoyi Xiu; Takayuki Shinohara; Masashi Matsuoka; Munenari Inoguchi; Ken Kawabe; Kei Horie. Collapsed Building Detection Using 3D Point Clouds and Deep Learning. Remote Sensing 2020, 12, 4057 .
AMA StyleHaoyi Xiu, Takayuki Shinohara, Masashi Matsuoka, Munenari Inoguchi, Ken Kawabe, Kei Horie. Collapsed Building Detection Using 3D Point Clouds and Deep Learning. Remote Sensing. 2020; 12 (24):4057.
Chicago/Turabian StyleHaoyi Xiu; Takayuki Shinohara; Masashi Matsuoka; Munenari Inoguchi; Ken Kawabe; Kei Horie. 2020. "Collapsed Building Detection Using 3D Point Clouds and Deep Learning." Remote Sensing 12, no. 24: 4057.
Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.
Ayub Mohammadi; Sadra Karimzadeh; Khalil Valizadeh Kamran; Masashi Matsuoka. Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran. Sensors 2020, 20, 7010 .
AMA StyleAyub Mohammadi, Sadra Karimzadeh, Khalil Valizadeh Kamran, Masashi Matsuoka. Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran. Sensors. 2020; 20 (24):7010.
Chicago/Turabian StyleAyub Mohammadi; Sadra Karimzadeh; Khalil Valizadeh Kamran; Masashi Matsuoka. 2020. "Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran." Sensors 20, no. 24: 7010.
Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are −10 and −98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz–Shabestar and Tabriz–Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are −13 and −120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).
Sadra Karimzadeh; Masashi Matsuoka. Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses. Sensors 2020, 20, 6913 .
AMA StyleSadra Karimzadeh, Masashi Matsuoka. Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses. Sensors. 2020; 20 (23):6913.
Chicago/Turabian StyleSadra Karimzadeh; Masashi Matsuoka. 2020. "Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses." Sensors 20, no. 23: 6913.
The advent of unmanned aerial system (UAS) has prompted close-range imagery a prevalent source to diversify spatial applications. In addition to nadir scenes, UAS is able to take oblique imagery, which increases the opportunity to acquire sophisticated spatial information from different viewing angles. These images provide more possibilities to reconstruct the land surfaces more completely in three-dimensions (3D). However, dealing with UAS imagery for 3D modelling has been a challenging task for years due to unstable and/or unknown exterior orientation parameters (EOPs) measured by direct georeferencing. With sequential oblique UAS imagery with inadequate or missing EOPs, this paper attempts to extract 3D spatial information from these types of images to achieve digital surface reconstruction. A modular workflow integrating the recovery of camera EOPs and 3D reconstruction in a relative space is presented. These images are spatially related by a feature-based incremental structure-from-motion (fi-SfM) for localization, stereo pairs selection and modification. Digital surface reconstruction, thenceforth, is addressed through dense matching and space intersection upon the outcomes of fi-SfM. The experimental results show that the designed schema is coherent in estimating the camera EOPs and modifying the inappropriate image pairs for improved 3D reconstruction. Furthermore, the surface model generated by discrete stereo pairs can be merged automatically to present a complete digital surface model (DSM). The completeness assessment has verified that the majority of the land surface can be successfully obtained by more than 90%, and the accuracy less than 1 (m) indicates that the implemented workflow can be used to achieve 3D modelling effectively.
Min-Lung Cheng; Masashi Matsuoka. Extracting three-dimensional (3D) spatial information from sequential oblique unmanned aerial system (UAS) imagery for digital surface modeling. International Journal of Remote Sensing 2020, 42, 1643 -1663.
AMA StyleMin-Lung Cheng, Masashi Matsuoka. Extracting three-dimensional (3D) spatial information from sequential oblique unmanned aerial system (UAS) imagery for digital surface modeling. International Journal of Remote Sensing. 2020; 42 (5):1643-1663.
Chicago/Turabian StyleMin-Lung Cheng; Masashi Matsuoka. 2020. "Extracting three-dimensional (3D) spatial information from sequential oblique unmanned aerial system (UAS) imagery for digital surface modeling." International Journal of Remote Sensing 42, no. 5: 1643-1663.
In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.
Sadra Karimzadeh; Masashi Matsuoka. Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring. Sensors 2020, 20, 4751 .
AMA StyleSadra Karimzadeh, Masashi Matsuoka. Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring. Sensors. 2020; 20 (17):4751.
Chicago/Turabian StyleSadra Karimzadeh; Masashi Matsuoka. 2020. "Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring." Sensors 20, no. 17: 4751.
In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.
Takayuki Shinohara; Haoyi Xiu; Masashi Matsuoka. FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning. Sensors 2020, 20, 3568 .
AMA StyleTakayuki Shinohara, Haoyi Xiu, Masashi Matsuoka. FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning. Sensors. 2020; 20 (12):3568.
Chicago/Turabian StyleTakayuki Shinohara; Haoyi Xiu; Masashi Matsuoka. 2020. "FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning." Sensors 20, no. 12: 3568.
A methodology for the automated identification of building damage from post-disaster aerial images was developed based on convolutional neural network (CNN) and building damage inventories. The aerial images and the building damage data obtained in the 2016 Kumamoto, and the 1995 Kobe, Japan earthquakes were analyzed. Since the roofs of many moderately damaged houses are covered with blue tarps immediately after disasters, not only collapsed and non-collapsed buildings but also the buildings covered with blue tarps were identified by the proposed method. The CNN architecture developed in this study correctly classifies the building damage with the accuracy of approximately 95 % in both earthquake data. We applied the developed CNN model to aerial images in Chiba, Japan, damaged by the typhoon in September 2019. The result shows that more than 90 % of the building damage are correctly classified by the CNN model.
Hiroyuki Miura; Tomohiro Aridome; Masashi Matsuoka. Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images. Remote Sensing 2020, 12, 1924 .
AMA StyleHiroyuki Miura, Tomohiro Aridome, Masashi Matsuoka. Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images. Remote Sensing. 2020; 12 (12):1924.
Chicago/Turabian StyleHiroyuki Miura; Tomohiro Aridome; Masashi Matsuoka. 2020. "Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images." Remote Sensing 12, no. 12: 1924.
We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu.
Erick Mas; Ryan Paulik; Kwanchai Pakoksung; Bruno Adriano; Luis Moya; Anawat Suppasri; Abdul Muhari; Rokhis Khomarudin; Naoto Yokoya; Masashi Matsuoka; Shunichi Koshimura. Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami. Pure and Applied Geophysics 2020, 177, 2437 -2455.
AMA StyleErick Mas, Ryan Paulik, Kwanchai Pakoksung, Bruno Adriano, Luis Moya, Anawat Suppasri, Abdul Muhari, Rokhis Khomarudin, Naoto Yokoya, Masashi Matsuoka, Shunichi Koshimura. Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami. Pure and Applied Geophysics. 2020; 177 (6):2437-2455.
Chicago/Turabian StyleErick Mas; Ryan Paulik; Kwanchai Pakoksung; Bruno Adriano; Luis Moya; Anawat Suppasri; Abdul Muhari; Rokhis Khomarudin; Naoto Yokoya; Masashi Matsuoka; Shunichi Koshimura. 2020. "Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami." Pure and Applied Geophysics 177, no. 6: 2437-2455.
The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)—an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1–Time 2 and Time 2–Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1–Time 2 and Time 2–Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN.
Raveerat Jaturapitpornchai; Poompat Rattanasuwan; Masashi Matsuoka; Ryosuke Nakamura. CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection. Remote Sensing 2020, 12, 990 .
AMA StyleRaveerat Jaturapitpornchai, Poompat Rattanasuwan, Masashi Matsuoka, Ryosuke Nakamura. CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection. Remote Sensing. 2020; 12 (6):990.
Chicago/Turabian StyleRaveerat Jaturapitpornchai; Poompat Rattanasuwan; Masashi Matsuoka; Ryosuke Nakamura. 2020. "CORN: An Alternative Way to Utilize Time-Series Data of SAR Images in Newly Built Construction Detection." Remote Sensing 12, no. 6: 990.
The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.
Bruno Adriano; Naoto Yokoya; Hiroyuki Miura; Masashi Matsuoka; Shunichi Koshimura. A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images. Remote Sensing 2020, 12, 561 .
AMA StyleBruno Adriano, Naoto Yokoya, Hiroyuki Miura, Masashi Matsuoka, Shunichi Koshimura. A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images. Remote Sensing. 2020; 12 (3):561.
Chicago/Turabian StyleBruno Adriano; Naoto Yokoya; Hiroyuki Miura; Masashi Matsuoka; Shunichi Koshimura. 2020. "A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images." Remote Sensing 12, no. 3: 561.
Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes.
Sadra Karimzadeh; Bakhtiar Feizizadeh; Masashi Matsuoka. DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran. ISPRS International Journal of Geo-Information 2019, 8, 537 .
AMA StyleSadra Karimzadeh, Bakhtiar Feizizadeh, Masashi Matsuoka. DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran. ISPRS International Journal of Geo-Information. 2019; 8 (12):537.
Chicago/Turabian StyleSadra Karimzadeh; Bakhtiar Feizizadeh; Masashi Matsuoka. 2019. "DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran." ISPRS International Journal of Geo-Information 8, no. 12: 537.
Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor’s wavelength (2.5λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is –0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.
Fumitaka Ogushi; Masashi Matsuoka; Marco Defilippi; Paolo Pasquali. Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach. Remote Sensing 2019, 11, 2467 .
AMA StyleFumitaka Ogushi, Masashi Matsuoka, Marco Defilippi, Paolo Pasquali. Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach. Remote Sensing. 2019; 11 (21):2467.
Chicago/Turabian StyleFumitaka Ogushi; Masashi Matsuoka; Marco Defilippi; Paolo Pasquali. 2019. "Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach." Remote Sensing 11, no. 21: 2467.
Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.
Sadra Karimzadeh; Masashi Matsuoka; Jianming Kuang; Linlin Ge. Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective. ISPRS International Journal of Geo-Information 2019, 8, 462 .
AMA StyleSadra Karimzadeh, Masashi Matsuoka, Jianming Kuang, Linlin Ge. Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective. ISPRS International Journal of Geo-Information. 2019; 8 (10):462.
Chicago/Turabian StyleSadra Karimzadeh; Masashi Matsuoka; Jianming Kuang; Linlin Ge. 2019. "Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective." ISPRS International Journal of Geo-Information 8, no. 10: 462.
This letter improves the computational efficiency and proposes a methodology to retrieve the missing feature pairs when utilizing binary-based features for image matching. The 64-byte feature descriptors of binary robust invariant scalable keypoints (BRISKs) are rearranged by combining human retina ganglion cells distribution and visual accommodation to speed up the image matching. In addition, an interactive two-sided matching is designed to determine the most probable keypoint pair when a feature point in the reference image is mapped to multiple candidates in the target image. Experimental results indicate that the proposed inverse sorting ring can reduce the processing time by more than 10% compared to the accelerated BRISK while maintaining the same reliability. Also, additional point-to-point feature pairs can be regained from the point-to-multicandidate cases by the proposed method in order to increase the number of matches.
Min-Lung Cheng; Masashi Matsuoka. An Enhanced Image Matching Strategy Using Binary-Stream Feature Descriptors. IEEE Geoscience and Remote Sensing Letters 2019, 17, 1253 -1257.
AMA StyleMin-Lung Cheng, Masashi Matsuoka. An Enhanced Image Matching Strategy Using Binary-Stream Feature Descriptors. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (7):1253-1257.
Chicago/Turabian StyleMin-Lung Cheng; Masashi Matsuoka. 2019. "An Enhanced Image Matching Strategy Using Binary-Stream Feature Descriptors." IEEE Geoscience and Remote Sensing Letters 17, no. 7: 1253-1257.
After a large-scale disaster, many damaged buildings are demolished and treated as disaster waste. Though the weight of disaster waste was estimated two months after the 2016 earthquake in Kumamoto, Japan, the estimated weight was significantly different from the result when the disaster waste disposal was completed in March 2018. The amount of disaster waste generated is able to be estimated by an equation by multiplying the total number of severely damaged and partially damaged buildings by the coefficient of generated weight per building. We suppose that the amount of disaster waste would be affected by the conditions of demolished buildings, namely, the areas and typologies of building structures, but this has not yet been clarified. Therefore, in this study, we aimed to use geographic information system (GIS) map data to create a time series GIS map dataset with labels of demolished and remaining buildings in Mashiki town for the two-year period prior to the completion of the disaster waste disposal. We used OpenStreetMap (OSM) data as the base data and time series SPOT images observed in the two years following the Kumamoto earthquake to label all demolished and remaining buildings in the GIS map dataset. To effectively label the approximately 16,000 buildings in Mashiki town, we calculated an indicator that shows the possibility of the buildings to be classified as the remaining and demolished buildings from a change of brightness in SPOT images. We classified 5701 demolished buildings from 16,106 buildings, as of March 2018, by visual interpretation of the SPOT and Pleiades images with reference to this indicator. We verified that the number of demolished buildings was almost the same as the number reported by Mashiki municipality. Moreover, we assessed the accuracy of our proposed method: The F-measure was higher than 0.9 using the training dataset, which was verified by a field survey and visual interpretation, and included the labels of the 55 demolished and 55 remaining buildings. We also assessed the accuracy of the proposed method by applying it to all the labels in the OSM dataset, but the F-measure was 0.579. If we applied test data including balanced labels of the other 100 demolished and 100 remaining buildings, which were other than the training data, the F-measure was 0.790 calculated from the SPOT image of 25 March 2018. Our proposed method performed better for the balanced classification but not for imbalanced classification. We studied the examples of image characteristics of correct and incorrect estimation by thresholding the indicator.
Yuzuru Kushiyama; Masashi Matsuoka. Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake. Remote Sensing 2019, 11, 2190 .
AMA StyleYuzuru Kushiyama, Masashi Matsuoka. Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake. Remote Sensing. 2019; 11 (19):2190.
Chicago/Turabian StyleYuzuru Kushiyama; Masashi Matsuoka. 2019. "Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake." Remote Sensing 11, no. 19: 2190.