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Improving Disaster Damage and Loss Assessments by Modeling and Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 47613

Special Issue Editors

International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai 980-8572, Japan
Interests: remote sensing; machine learning; numerical simulation; disaster science
Special Issues, Collections and Topics in MDPI journals
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166/15731, Iran
Interests: SAR remote sensing; damage assessment; site characterization
Special Issues, Collections and Topics in MDPI journals
Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation, National University of Engineering, Lima 15333, Peru
Interests: remote sensing; machine learning; earthquake engineering; intelligent evacuation systems
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
Interests: machine learning; remote sensing and gis; image processing; environmental modelling; object detection
Special Issues, Collections and Topics in MDPI journals
Civil Engineering, Indian Institute of Technology, Roorkee 247667, India
Interests: rapid response to natural hazards; synthetic aperture radar remote sensing; application of AI to understand science of natural hazards
School of Statistics, Renmin University of China, Beijing 100872, China
Interests: catastrophe risk management; remote sensing; deep learning

Special Issue Information

Dear Colleagues,

In recent years, remote sensing (RS) technologies have been used extensively in scientific and engineering applications, together with physics-based and advanced machine learning modeling. Particularly, disaster science research has seen considerable attention, yielding novel methodologies for rapid post-disaster damage assessments and an accurate understanding of hazard scenarios before disasters. On the one hand, integrating numerical modeling and remote sensing technologies grant powerful means to analyze several characteristics of the Earth’s surface ground, such as deformations, growing urban environments, and local site characterization. Furthermore, optical imaging and synthetic aperture radar (SAR) provided complementary information for pre- and post-disaster hazard assessments. However, complex and unique disasters induced by earthquakes, heavy rain, and other natural phenomena present great challenges for RS and modeling technologies because of several factors such as data accessibility, missing information, and high computation costs. This Special Issue explores the theory and application of numerical modeling with RS technologies for disaster damage and loss assessments.
This Special Issue is open to all contributions on recent advances and novel developments of methodologies and best-case study applications in the RS and numerical and machine learing modeling of earthquakes, tsunamis, volcanic, and flooding events. We encourage submissions of both review and original research articles related, but not limited to, the following topics:

  • Analysis of urban environment changes;
  • RS for urban vulnerability analysis;
  • Damage recognition following major disasters;
  • Machine learning for disaster research;
  • Detection and classification of building damage;
  • Extraction of flooded areas from RS data;
  • Time series data for surface deformation monitoring;
  • Open data and big data for multi-hazard analysis;
  • Natural hazard modelling and prediction.

Dr. Bruno Adriano
Dr. Sadra Karimzadeh
Dr. Luis Moya
Dr. Bahareh Kalantar
Dr. Alok Bhardwaj
Dr. Yanbing Bai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Synthetic aperture radar
  • Optical imaging
  • Earthquake damage
  • Tsunami events
  • Flood damage
  • Loss estimation
  • Machine learning

Published Papers (11 papers)

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25 pages, 45315 KiB  
Article
Monitoring Land Subsidence Using PS-InSAR Technique in Rawalpindi and Islamabad, Pakistan
by Junaid Khan, Xingwei Ren, Muhammad Afaq Hussain and M. Qasim Jan
Remote Sens. 2022, 14(15), 3722; https://doi.org/10.3390/rs14153722 - 03 Aug 2022
Cited by 14 | Viewed by 2905
Abstract
Land subsidence is a major concern in vastly growing metropolitans worldwide. The most serious risks in this scenario are linked to groundwater extraction and urban development. Pakistan’s fourth-largest city, Rawalpindi, and its twin Islamabad, located at the northern edge of the Potwar Plateau, [...] Read more.
Land subsidence is a major concern in vastly growing metropolitans worldwide. The most serious risks in this scenario are linked to groundwater extraction and urban development. Pakistan’s fourth-largest city, Rawalpindi, and its twin Islamabad, located at the northern edge of the Potwar Plateau, are witnessing extensive urban expansion. Groundwater (tube-wells) is residents’ primary daily water supply in these metropolitan areas. Unnecessarily pumping and the local inhabitant’s excessive demand for groundwater disturb the sub-surface’s viability. The Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) approach, along with Sentinel-1 Synthetic Aperture Radar (SAR) imagery, were used to track land subsidence in Rawalpindi-Islamabad. The SARPROZ application was used to study a set of Sentinel-1 imagery obtained from January 2019 to June 2021 along descending and ascending orbits to estimate ground subsidence in the Rawalpindi-Islamabad area. The results show a significant increase (−25 to −30 mm/yr) in subsidence from −69 mm/yr in 2019 to −98 mm/yr in 2020. The suggested approach effectively maps, detects, and monitors subsidence-prone terrains and will enable better planning, surface infrastructure building designs, and risk management related to subsidence. Full article
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24 pages, 3934 KiB  
Article
Do We Need a Higher Resolution? Case Study: Sentinel-1-Based Change Detection of the 2018 Hokkaido Landslides, Japan
by István Péter Kovács, Giulia Tessari, Fumitaka Ogushi, Paolo Riccardi, Levente Ronczyk, Dániel Márton Kovács, Dénes Lóczy and Paolo Pasquali
Remote Sens. 2022, 14(6), 1350; https://doi.org/10.3390/rs14061350 - 10 Mar 2022
Cited by 2 | Viewed by 2756
Abstract
Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of publications on the role of [...] Read more.
Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of publications on the role of S1 in landslide detection, there is still a need for research to further clarify the capabilities of the sensor and the applicable image analysis techniques. Previous studies have successfully exploited high-resolution ALOS-PALSAR image-based intensity and coherence analysis at the 2018 Hokkaido landslides. Nevertheless, they expressed a clear need to analyse the capabilities of other sensors (such as S1). This raises the question: Do we need SAR imagery with higher spatial resolution (such as ALOS-PALSAR) or are freely available S1 imagery also suitable for rapid landslide detection? The S1 images could provide suitable material for a comparative analysis and could answer the aforementioned question. Therefore, 17 ascending and 19 descending S1 images were analysed to test S1 accuracy on landslide detection. Multitemporal analyses of both intensity and coherence were performed along with coherence differences, multitemporal features (MTF) and MTF differences of coherence images. In addition, the spatial analysis of the classification results was also evaluated to highlight the potential of S1 coherence analysis. S1 was found to have limitations at the site, as single coherence differences provided low-quality results. However, the results were significantly improved by calculating the MTF on coherence and almost reached the success rate of the ALOS-PALSAR-based coherence analysis, even though the improvement of the results with intensity was not possible. Half of the false positives were identified in the 30–45-m buffer zone of the agreement, underlining that the spatial resolution of the S1 is not appropriate for accurate landslide detection. Only an approximation of the landslide-affected area can be given with considerable overestimation. Due to the inclusion of post-event images, the sensor is not perfectly applicable for rapid detection purposes here. Full article
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22 pages, 8841 KiB  
Article
Synergistic Use of Geospatial Data for Water Body Extraction from Sentinel-1 Images for Operational Flood Monitoring across Southeast Asia Using Deep Neural Networks
by Junwoo Kim, Hwisong Kim, Hyungyun Jeon, Seung-Hwan Jeong, Juyoung Song, Suresh Krishnan Palanisamy Vadivel and Duk-jin Kim
Remote Sens. 2021, 13(23), 4759; https://doi.org/10.3390/rs13234759 - 24 Nov 2021
Cited by 12 | Viewed by 2488
Abstract
Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not [...] Read more.
Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation. Full article
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31 pages, 2832 KiB  
Article
Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
by Mohsen Alizadeh, Hasan Zabihi, Fatemeh Rezaie, Asad Asadzadeh, Isabelle D. Wolf, Philip K Langat, Iman Khosravi, Amin Beiranvand Pour, Milad Mohammad Nataj and Biswajeet Pradhan
Remote Sens. 2021, 13(22), 4519; https://doi.org/10.3390/rs13224519 - 10 Nov 2021
Cited by 10 | Viewed by 3592
Abstract
Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural [...] Read more.
Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies. Full article
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21 pages, 12344 KiB  
Article
Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
by Davoud Omarzadeh, Sadra Karimzadeh, Masashi Matsuoka and Bakhtiar Feizizadeh
Remote Sens. 2021, 13(21), 4272; https://doi.org/10.3390/rs13214272 - 24 Oct 2021
Cited by 11 | Viewed by 2883
Abstract
This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of [...] Read more.
This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies. Full article
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23 pages, 27177 KiB  
Article
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia
by Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Saeid Janizadeh, Fariborz Shabani, Kourosh Ahmadi and Farzin Shabani
Remote Sens. 2021, 13(13), 2638; https://doi.org/10.3390/rs13132638 - 05 Jul 2021
Cited by 49 | Viewed by 4852
Abstract
Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in [...] Read more.
Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods. Full article
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20 pages, 14114 KiB  
Article
Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets
by Yanbing Bai, Wenqi Wu, Zhengxin Yang, Jinze Yu, Bo Zhao, Xing Liu, Hanfang Yang, Erick Mas and Shunichi Koshimura
Remote Sens. 2021, 13(11), 2220; https://doi.org/10.3390/rs13112220 - 05 Jun 2021
Cited by 37 | Viewed by 6835
Abstract
Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in [...] Read more.
Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability. Full article
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25 pages, 10970 KiB  
Article
Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
by Amy L. Parker, Pascal Castellazzi, Thomas Fuhrmann, Matthew C. Garthwaite and Will E. Featherstone
Remote Sens. 2021, 13(8), 1422; https://doi.org/10.3390/rs13081422 - 07 Apr 2021
Cited by 12 | Viewed by 6518
Abstract
Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where [...] Read more.
Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where the use of SAR data is limited. Consequently, less is known about how these data apply in the Australian context, how they could aid national hazard monitoring and assessment, and what new insights could be gleaned for the benefit of the international disaster risk reduction community. The European Space Agency Sentinel-1 satellite mission now provides the first spatially and temporally complete global SAR dataset and the first opportunity to use these data to systematically assess hazards in new locations. Using the example of Australia, where floods and uncontrolled bushfires, earthquakes, resource extraction (groundwater, mining, hydrocarbons) and geomorphological changes each pose potential risks to communities, we review past usage of EO for hazard monitoring and present a suite of new case studies that demonstrate the potential added benefits of SAR. The outcomes provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context. Future opportunities to improve national hazard identification will arise from: new SAR sensing capabilities, which for Australia includes a first-ever civilian EO capability, NovaSAR-1; the integration of Sentinel-1 SAR with other EO datasets; and the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes to support operational applications. Full article
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15 pages, 10271 KiB  
Article
The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework
by Genki Okada, Luis Moya, Erick Mas and Shunichi Koshimura
Remote Sens. 2021, 13(7), 1401; https://doi.org/10.3390/rs13071401 - 05 Apr 2021
Cited by 9 | Viewed by 4961
Abstract
When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning [...] Read more.
When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response. Full article
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19 pages, 10707 KiB  
Article
Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery
by Mahdi Hasanlou, Reza Shah-Hosseini, Seyd Teymoor Seydi, Sadra Karimzadeh and Masashi Matsuoka
Remote Sens. 2021, 13(6), 1195; https://doi.org/10.3390/rs13061195 - 20 Mar 2021
Cited by 24 | Viewed by 4794
Abstract
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 [...] Read more.
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%. Full article
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18 pages, 11073 KiB  
Technical Note
A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia
by Sadra Karimzadeh and Masashi Matsuoka
Remote Sens. 2021, 13(12), 2267; https://doi.org/10.3390/rs13122267 - 09 Jun 2021
Cited by 5 | Viewed by 2903
Abstract
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 [...] Read more.
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). Full article
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