劉 ウェン

Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

2016年4月熊本地震では，熊本県熊本地方を震源とするMw6.2の地震が発生し，その約16時間後に同地域を震源とするMw7.0の地震が発生した．本研究では，地震による建物の被害状況を効率的にかつ安全に把握する方法として，航空レーザ測量データを深層学習することによって建物被害検出モデルの構築を試みた．本震前後に収集した航空レーザ測量データに対し，深層学習のアルゴリズムの一つである畳み込みニューラルネットワーク（CNN）を適用し，ネットワーク構成を変えながら最良のモデルの検討を行った．その結果，正答率が90%を超えるモデルを構築することができた．

現在，日本の水道では年間2万件を超える漏水・破損事故が発生している．上水道管の漏水は，地上に流れ出す地上漏水と，地上には流れ出さず地下で流れている地下漏水の2種類に大別できる．地上漏水は人目に触れることから発見しやすいものの，地下漏水は漏水の状況を直接目視で確認できないため，早期発見のための技術開発が求められている．そこで本研究では，現在普及が進んでいるスマートメータを活用した水道管路のモニタリングを想定し，管網端部の水圧情報を使用した漏水位置予測に関する検討を行った．漏水シナリオや機械学習手法の異なる6つのモデルを構築し，その予測精度を比較した．水圧変化率，水圧変化量，管種情報を説明変数とし，LightGBMに基づき構築した漏水予測モデルが最も良好な結果を示した．

Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs multiscale attention to merge the weighting outputs of adjacent multiscale inputs, and dilated convolution to capture spatial-context information. The performance analysis shows that the proposed MSA-DCNN yields the best results by combining multiscale attention and dilated convolution. Additionally, the proposed method gains the mIoU of 74.88%, which is a significant improvement over the existing techniques.

Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.

The ability of drones to access disaster areas has been proven powerful and flexible for acquiring first-hand optical imagery data for environmental observation. However, such imagery data usually undergo postprocessing, and the three-dimensional (3D) products are mainly for accurate land surveys. The postprocessing procedure is too time-consuming to meet instant decision support and rescue response requirements. Therefore, this paper intends to develop a systematic workflow that is able to achieve on-the-fly 3D reconstruction in disaster areas by optical imagery sequentially acquired by drones. This study proposes a strategy to spatially link sequential images (SLSI) for image localization and suitable stereopair selection. In addition, the criteria for valid epipolar stereoapair determination are developed to make the 3D dense reconstruction more automatic and effective. The 3D digital land surface can be gradually reconstructed and expanded in the computer system while the drone is capturing new images. This paper utilizes the imagery dataset of collapsed buildings induced by the 2016 Kumamoto earthquake in Japan to simulate the more effective 3D reconstruction. Although the accuracy of the consequence is reported to be closely one meter, the mean data processing time for every image can achieve the level by approximately ten seconds while performing the proposed scheme on an iMac with Intel Core i5 and 16 GB random access memory (RAM). As a result, the efficiency and computational power needed are significantly reduced to support emergency applications soon after a disaster occurs.

Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.

Remote sensing is useful to assess damage situations due to natural disasters. In this study, Synthetic Aperture Radar (SAR) images acquired from PALSAR-2 sensor onboard ALOS-2 satellite were used to observe damage situations due to the 13 February 2021 Off-Fukushima, Japan, earthquake (Mw7.1). Landslides and rockfalls were reported at the Joban Expressway, a motor racing circuit, and seaside cliffs. Change detection using pre- and post-event PALSAR-2 intensity images was carried out and the results were compared with airborne optical images and field survey data. The landslide at the expressway and the motor circuit were recognized from the L-band SAR intensity data, but the rockfalls and other small-scale damages could not be identified due to the limitation of spatial resolution.

Airborne Synthetic Aperture Radar (SAR) sensors are suitable for collecting information in an emergency response phase since they can acquire high-resolution images regardless of weather and sunlight conditions. Due to the Kumamoto earthquakes on April 14 and 16, 2016, scores of bridges were damaged and around 200 landslides occurred. In this study, we set the target area in Minami-Aso Village, Kumamoto Prefecture, Japan, which was suffered from numerous landslides. The landslide areas including several affected bridges were investigated using the pre-and post-event Pi-SAR2 airborne SAR images. As a result, the HH polarization and the surface scattering component were found to be most suitable to detect landslides by the pre-and post-event difference. The collapsed bridges were also extracted properly using the contribution ratio of each scattering component within bridge outlines.

平成30年7月豪雨は，西日本を中心に広範囲に甚大な被害をもたらした．このような豪雨の発生は増加傾向であり，土砂崩壊発生地点を予測することは重要である．現在行われている土砂災害警戒区域等の指定は，労力と時間が非常にかかるうえ，指定状況に関する課題もある．そこで本研究では，機械学習の一手法であるランダムフォレストを用いて，土砂崩壊発生地点の予測モデルを構築した．説明変数の異なる2つのモデルを作成し，その予測精度を比較した．また，近年研究が進められている説明可能AI（XAI）の一つであるSHAPを用いて大域説明，局所説明を行い，予測結果の判断根拠を明らかにした上で2つのモデルの結果に差異が生じた理由の考察を行った．

2018年北海道胆振東部地震では，厚真町やその周辺で斜面崩壊が多発し，多くの犠牲者が発生した．既往研究では，斜面崩壊予測方法が提案され推定精度に関する検討が行われてきたが，広域に適用可能な予測方法は未だ研究段階である．そこで本研究では，機械学習を用いて北海道胆振東部地震における斜面崩壊の推定を行い，その精度を検証した．さらに，衛星画像が地震後早期に取得できたものと仮定し，正規化植生指数（NDVI）を機械学習の説明変数に用いることを検討した．推定結果をκ係数等に基づき評価すると，ランダムフォレストによる推定結果が最も精度が高く，特にNDVIが利用できる場合は推定結果と実際の斜面崩壊の一致度が良好であった．

東北地方太平洋沖地震では，東日本の広域にわたって液状化が発生した．現在の液状化予測では詳細な地盤調査データを利用した手法がよく用いられているが，このような手法を広域に適用することは困難である．そのため，道路や水道，都市ガスのようなライフラインネットワークの被害予測には，簡便に面的な評価を行える手法の確立が望まれる．そこで，本研究ではサポートベクターマシンとランダムフォレストを用いて，液状化発生予測モデルを構築することを目的とする．既往研究の検討結果を踏まえて，液状化の発生に影響がある地震動継続時間を変数として新たに加えた．また，液状化の発生傾向によってモデルを微地形区分に基づきグループ分けすることを検討した．

Typhoon Hagibis passed through Japan on October 12, 2019, brought heavy rainfalls over half of Japan, and resulted in flooding in wide areas. This study focuses on a flooded state-managed river, the Naka River, Ibaraki Prefecture, Japan. In this study, we conducted the field survey on October 28, 2019, after the typhoon passed. Additionally, a series of runoff analyses for the entire Naka River basin and inundation analyses for some sections of the Naka River were conducted. Comparing the results of numerical simulation with the inundated areas detected by aerial and satellite Synthetic Aperture Radar (SAR) images, the accuracy of the numerical simulation was investigated. The maximum F value was 78.65%, and the simulated result broadly coincided with the inundated areas.

Bridges are an important part of road networks in an emergency period, as well as in ordinary times. Bridge collapses have occurred as a result of many recent disasters. Synthetic aperture radar (SAR), which can acquire images under any weather or sunlight conditions, has been shown to be effective in assessing the damage situation of structures in the emergency response phase. We investigate the backscattering characteristics of washed-away or collapsed bridges from the multi-temporal high-resolution SAR intensity imagery introduced in our previous studies. In this study, we address the challenge of building a model to identify collapsed bridges using five change features obtained from multi-temporal SAR intensity images. Forty-four bridges affected by the 2011 Tohoku-oki earthquake, in Japan, and forty-four bridges affected by the 2020 July floods, also in Japan, including a total of 21 collapsed bridges, were divided into training, test, and validation sets. Twelve models were trained, using different numbers of features as input in random forest and logistic regression methods. Comparing the accuracies of the validation sets, the random forest model trained with the two mixed events using all the features showed the highest capability to extract collapsed bridges. After improvement by introducing an oversampling technique, the F-score for collapsed bridges was 0.87 and the kappa coefficient was 0.82, showing highly accurate agreement.

Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis.

The extraction of roads and centerlines from aerial imagery is considered an important topic because it contributes to different fields, such as urban planning, transportation engineering, and disaster mitigation. Many researchers have studied this topic as a two-separated task that affects the quality of extracted roads and centerlines because of the correlation between these two tasks. Accurate road extraction enhances accurate centerline extraction if these two tasks are processed simultaneously. This study proposes a multitask learning scheme using a gated deep convolutional neural network (DCNN) to extract roads and centerlines simultaneously. The DCNN is composed of one encoder and two decoders implemented on the U-Net backbone. The decoders are assigned to extract roads and centerlines from low-resolution feature maps. Before extraction, the images are processed within an encoder to extract the spatial information from a complex, high-resolution image. The encoder consists of the residual blocks (Res-Block) connected to a bridge represented by a Res-Block, and the bridge connects the two identical decoders, which consists of stacking convolutional layers (Conv.layer). Attention gates (AGs) are added to our model to enhance the selection process for the true pixels that represent road or centerline classes. Our model is trained on a dataset of high-resolution aerial images, which is open to the public. The model succeeds in efficiently extracting roads and centerlines compared with other multitask learning models.

Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure.

Record-breaking heavy rainfall hit Japan from July 3 to 31, 2020. The water level of rivers rose rapidly, and many bridges were washed away. In this study, two temporal ALOS-2 PALSAR-2 intensity images were introduced to detect damaged bridges over the Kuma River in Yatsushiro City and Ashikita Town, Kumamoto Prefecture, Japan. First, the backscattering models of bridges were investigated. Then the damage conditions of twenty-five target bridges were examined by the difference and correlation coefficient in the backscatter intensity. In addition, the increases of water levels was estimated by the movements of bridges’ SAR model. A field survey report and a post-event SPOT-7 satellite optical image were used to verify our results.

Remote sensing is useful to extract damages due to natural disasters. In this study, Synthetic Aperture Radar (SAR) images acquired from PALSAR-2 sensor onboard ALOS-2 satellite were used to observe damage situations due to the 13 February 2021 Off-Fukushima, Japan, earthquake. Landslides and rockfalls were reported at the Joban Expressway, a motor racing circuit, and seaside cliffs. Change detection using pre- and post-event PALSAR-2 intensity images was carried out and the results were compared with airborne optical images and field survey data. The landslide at the expressway and the motor circuit were recognized from the SAR intensity data, but rockfalls and other small-scale damages could not be identified due to the limitation of spatial resolution.

This paper investigates the 2016 Kumamoto Earthquake to explore the use of post-event full polarimetric SAR images in building damage assessment. In this study, one post-event airborne SAR image of Pi-SAR-X2 was employed to detect the changes of residential buildings in Mashiki Town, Kumamoto Prefecture, Japan. The changes in scattering characteristics of collapsed and non-collapsed buildings were compared using variance, homogeneity, contrast and dissimilarity textures of the Grey Level Co-occurrence Matrix (GLCM) from building footprints and roof areas. In addition, due to the difficulty in detection using only one texture, combination of textures in different polarizations were also applied for damage detection. The results of this method were then compared with the field survey data. The result indicates the possibility of building damage assessment using only post-event data and solving the pre-event data limitation problem.

<p>本研究では，地震により被災した建物を対象に実施される住家の被害認定調査の効率化を目的とし，深層学習のアルゴリズムの一つである畳み込みニューラルネットワーク（CNN）を適用した建物の被災度判別を試みた．まず，2016年熊本地震後に実施された被害認定調査における建物の外観画像からデータセットを作成した．そのデータセットにCNNを適用し，対象建物の全壊・非全壊を判別するモデルの構築に向けた検討を進めた．画像数の不足に伴う過学習を抑制するために，他の地震で被災した建物の画像データを追加し，判別モデルの精度向上を図ることができた．</p>

An Mw 6.6 earthquake struck the eastern Iburi area of Hokkaido, Japan, on September 6, 2018. Due to the strong shaking, more than 3000 landslides occurred around Atsuma Town and killed 36 people. In this study, two pre-event and one post-event ALOS-2 PALSAR-2 images were used to extract landslides in Abira, Atsuma and Mukawa Towns. The characteristics of landslides were investigated using the backscattering coefficient, the coherence and the slope of elevation. Then the landslides in the target area were extracted by the optimal threshold values. A procedure using both the difference of backscattering coefficient and the difference of coherence was proposed to extract landslides. Finally, the result was verified by comparing with a post-event optical satellite image and a landslide distribution map by the GSI.

<p> 令和元年房総半島台風と命名された2019年台風15号は，非常に強い勢力を保ったまま9月9日に千葉県千葉市に上陸した．千葉県内では7万棟以上の住家が一部損壊以上の被害を受けた．そこで本研究では，国際航業（株）と朝日航洋（株）がそれぞれ9月19～20日，9月27～28日に撮影した航空写真を用いて，千葉県房総半島の建物の屋根損傷部に覆われたブルーシートを抽出した．一部地域で目視判読を行い，それを検証用データとしてブルーシートの自動抽出方法を提案した．なお，国際航業と朝日航洋が撮影した航空写真の撮影条件が異なるため，両者には異なる自動抽出方法を使用した．最後に，ブルーシートの面積と屋根投影面積によって算出したブルーシート被覆率を最大瞬間風速と比較し，最大瞬間風速による屋根のブルーシート被覆率の予測関数を構築した．</p>

© The Author(s) 2020. Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision makers and for producing an effective emergency response. In this vein, this article presents the following: (1) statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) an investigation of the factors influencing the extent of debris; (3) probability functions for the debris extent; and (4) applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (16 April 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements in these regards. One of these techniques is the use of deep convolution neural networks (DCNNs). This study presents a road segmentation model consisting of a skip connection of U-net and residual blocks (ResBlocks) in the encoding part and convolution layers (Conv. layer) in the decoding part. Although the model uses fewer residual blocks in the encoding part and fewer convolution layers in the decoding part, it produces better image predictions in comparison with other state-of-the-art models. This model automatically and efficiently extracts road networks from high-resolution aerial imagery in an unexpansive manner using a small training dataset.

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth's surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (M-w 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

A series of earthquakes hit Kumamoto Prefecture, Japan, continuously over a period of two days in April 2016. The earthquakes caused many landslides and numerous surface ruptures. In this study, two sets of the pre- and post-event airborne Lidar data were applied to detect landslides along the Futagawa fault. First, the horizontal displacements caused by the crustal displacements were removed by a subpixel registration. Then, the vertical displacements were calculated by averaging the vertical differences in 100-m grids. The erosions and depositions in the corrected vertical differences were extracted using the thresholding method. Slope information was applied to remove the vertical differences caused by collapsed buildings. Then, the linked depositions were identified from the erosions according to the aspect information. Finally, the erosion and its linked deposition were identified as a landslide. The results were verified using truth data from field surveys and image interpretation. Both the pair of digital surface models acquired over a short period and the pair of digital terrain models acquired over a 10-year period showed good potential for detecting 70% of landslides.

A series of heavy rainfalls hit the western half of Japan from June 28 to July 8, 2018. Increased river water overflowed and destroyed river banks, causing flooding over vast areas. In this study, two pre-event and one co-event ALOS-2 PALSAR-2 images were used to extract inundation areas in Kurashiki and Okayama Cities, Okayama Prefecture, Japan. First, water regions were extracted by threshold values from three-temporal intensity images. The increased water regions in July 2018 were obtained as inundation. Inundated built-up areas were identified by the increase of backscattering intensity. Differences between the pre-and co-event coherence values were calculated. The area with decreased coherence was extracted as a possible inundation area. The results of a field survey conducted on July 16, 2018 were used to estimate the optimal parameters for the extraction. Finally, the results from the intensity and coherence images were verified by making comparisons between a web-based questionnaire survey report and the visual interpretation of aerial photographs.

© 2019 The Author(s) With the remarkable progress in access to remote sensing imagery data, nowadays research very often utilizes more than one image. We are often able to use multitemporal, hyperspectral, and/or full polarization of microwave radar images. In addition, it has become the general consensus that texture analysis plays an important role in remote sensing. It has been found in several publications that texture analysis was applied to each layer separately; however, this procedure requires a significant amount of computation and produces a massive volume of data. One alternative, and perhaps a better procedure, is to arrange the images into a multi-layered structure and perform texture analysis within some sort of three-dimensional domain. This manuscript extends the concepts of the gray level of co-occurrence matrix (GLCM) texture analysis applied for a single image to a multi-layered set of images, referred to in this paper as 3DGLCM. We then presented an interpretation of the 3DGLCM within the context of building damage identification. A set of 3DGLCM-based features were computed and evaluated as well. As a result, it was observed that some texture features have certain similarities with other methods proposed in previous studies, whereas other features have not been used before. Furthermore, this paper evaluates the performance of the Support Vector Machine (SVM) classifier in learning and detecting collapsed buildings using 3DGLCM-based features. Thus, the empirical evaluation focuses on the identification of collapsed buildings caused by the 2011 Tohoku earthquake and tsunami, where individual polarized TerraSAR-X intensity images are used to compute the texture features, and the collapsed buildings caused by the 2016 Kumamoto earthquake, where LIDAR-based digital surface models are used to compute the texture features. Extensive datasets consisting of building damage states that have been visually inspected by local authorities and research teams are used to set up the training and testing subsets. Furthermore, the proposed texture features are compared with features commonly used to identify collapsed buildings. The study concludes that an SVM trained with 3DGLCM-based features identifies collapsed buildings with high accuracy and outperforms an SVM trained with common features used in previous studies.

<p>本研究では，2016年熊本地震における熊本県益城町の家屋被害認定調査結果に基づいて建物被害分析を行うとともに，推定地震動分布と組み合わせて建物被害関数を構築した．建物被害分析では構造別，建築年代別，木造建物の屋根形式別・階数別に被害を分析した．その結果，木造建物の全壊率は，RC造，S造，LS造と比較して全体的に大きく，建築年代が古くなるほど大きくなる傾向が顕著にみられた．また，最大地表速度および計測震度に対する，構造別，木造の建築年代別の益城町の建物被害関数を構築した．益城町の被害関数は，1995年兵庫県南部地震の結果に基づく経験式と比べて，同一の最大地表速度における全壊率が低くなる傾向がみられた．</p>

© 2018 The Author(s) Remote sensing satellite imagery plays an important role in estimating collapsed buildings in the aftermath of a large-scale disaster. However, some previous methodologies are restricted to using specific radar sensors. Others methods, such as machine learning algorithms, require training data, which are extremely difficult to obtain immediately after a disaster. This paper proposes a novel method to extract collapsed buildings based on the integration of satellite imagery, the spatial distribution of a demand parameter, fragility functions, and a geospatial building inventory. The proposed method is applicable regardless of the type of radar sensor and does not require any training data. The method was applied to extract buildings that collapsed during the 2011 Great East Japan Tsunami. The results showed that the proposed method is effective and consistent with the surveyed building damage data.

© 2019 by the authors. In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993-2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006-2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014-2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.

© 2018 Author(s). Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015. Due to the record-breaking amount of rainfall, several riverbanks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor on board the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in the city of Joso, Ibaraki Prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10 m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal synthetic aperture radar (SAR) images. The extracted results were modified by considering the land cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and more than 85 % of the maximum inundation areas were extracted successfully.

Infrastructures in the world get old in several decades of their service time. Falling-offs of parts of deteriorated structures were often reported and sometimes caused casualties in Japan and many other countries. When an earthquake occurs, in particular, deteriorated structures have higher possibility to be damaged or collapsed. Thus assessing the health condition of structures is one of the important topics in civil engineering. Considering a large number of structures that have been in service more than 40 years in Japan, efficient evaluation methods are requested. In this regard, non-destructive tests have high possibility to be applied to various structures without affecting their functions. Accordingly, this study focuses on the use of infrared thermography to detect internal deterioration of concrete structures. As a first step of investigation, thermography diagnosis, hammer sounding test and Schmidt rebound hammer test were carried out to detect internal deterioration of a concrete retaining wall located in the campus of Chiba University, Chiba, Japan, and the results were compared to evaluate the capability and accuracy of these diagnosis methods.

© 2018, Fuji Technology Press. All rights reserved. Since synthetic aperture radar (SAR) sensors onboard satellites can work under all weather and sunlight conditions, they are suitable for information gathering in emergency response after disasters occur. This study attempted to extract collapsed bridges in Iwate Prefecture, Japan, which was affected by more than 15-m high tsunamis due to the Mw 9.0 earthquake on March 11, 2011. First, the locations of the bridges were extracted using GIS data of roads and rivers. Then, we attempted to detect the collapsed or washed-away bridges using visual interpretation and thresholding methods. The threshold values on the SAR backscattering coefficients and the percentage of non-water regions were applied to the post-event high-resolution TerraSAR-X images. The results were compared with the optical images and damage investigation reports. The effective use of a single SAR intensity image in the extraction of collapsed bridges was demonstrated with a high overall accuracy of more than 90%.

© Author(s) 2018. The 2016 Kumamoto earthquake sequence was triggered by an Mw 6.2 event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April, an Mw 7.0 event (the mainshock) followed. The epicenters of both events were located near the residential area of Mashiki and affected the region nearby. Due to very strong seismic ground motion, the earthquake produced extensive damage to buildings and infrastructure. In this paper, collapsed buildings were detected using a pair of digital surface models (DSMs), taken before and after the 16 April mainshock by airborne light detection and ranging (lidar) flights. Different methods were evaluated to identify collapsed buildings from the DSMs. The change in average elevation within a building footprint was found to be the most important factor. Finally, the distribution of collapsed buildings in the study area was presented, and the result was consistent with that of a building damage survey performed after the earthquake.

Extraction of landslides from a pair of Lidar data taken before and after the 2016 Kumamoto, Japan, earthquake was carried out. The spatial correlation coefficient of the two Lidar data was calculated, and the horizontal shift of the April-23 DSM with the maximum correlation coefficient was considered as the crustal movement by the April-16 main-shock. By taking the difference of the co-registered DSMs, the change of the surface elevation was calculated. This elevation change includes many effects due to the earthquake, such as landslides and building collapses, and the other temporal changes, such as parking cars and construction/rescue activities. Thus in this study, only large-scale elevation changes more than plus and minus 2.0 m and the areas of larger than 200 square meters were extracted as possible landslides. The extracted areas were compared with aerial photos taken after the Kumamoto earthquake and other soil movement maps made for this event. The result shows that large-scale landslides were easily extracted by the difference of the DSMs and even ground deformations along surface ruptures, where trees were torn down, could be identified.t

<p>Owing to the remarkable improvements in radar sensors, it is now possible to obtain information regarding a single structure from high-resolution SAR images. In our previous research, we proposed a method for detecting the heights of low-rise buildings automatically using 2D GIS data and a single high-resolution TerraSAR-X intensity image. However, it was difficult to apply this method to high-rise buildings due to their backscattering characteristics. In this study, a new method was developed for estimating the heights of high-rise buildings based on the results from an Interferometric SAR (InSAR) analysis. The potential layover areas were extracted using both amplitude and phase characteristics. First, the proposed method for low-rise buildings was used to extract the layovers from one intensity image. The phase characteristics in the InSAR result were then investigated and used to extract potential layover areas. Finally, heights were estimated based on the layover lengths obtained from both the intensity and phase images. The developed method was tested on two TerraSAR-X image sets of central Tokyo, Japan, in the HighSpot mode. The results were verified by comparison with a digital surface model obtained by stereoscopic photogrammetry. The detected heights were found to be reasonable.</p>

High-resolution commercial synthetic aperture radar (SAR) satellites with resolutions of several meters have recently been used for effective disaster monitoring. One study reported the earthquake's displacement using the pixel matching method with both pre-and postevent TerraSAR-X data, with a validated accuracy of similar to 30 cm at global navigation satellite system (GNSS) Earth observation network (GEONET) reference points. However, it is insufficient to determine the accuracy using analysis of only a couple of data points per orbit. In addition, the errors were not reported because the number of data samples was too small to discuss the statistics. In order to better understand displacement accuracy, we analyzed displacement features using the pixel matching method to evaluate the relative geolocation accuracies of the TerraSAR-X product. First, we used fast Fourier transform oversampling 16 times to develop the pixel matching method for estimating the displacement at the subpixel level using the TerraSAR-X StripMap dataset. Second, we applied this methodology to 20 pairs of images from the Tokyo metropolitan area and calculated the displacement for each image pair. Third, we conducted spatial and temporal analyses in order to understand the displacement features. Finally, we evaluated the displacement accuracy by comparison with GEONET and solid earth tide data as a reference. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

Synthetic Aperture Radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with various geophysical processes. To investigate the land-surface deformation in Karamay, a typical oil-producing city in the Xinjiang Uyghur Autonomous Region, China, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired for the period from 2007 to 2009, and a two-pass differential SAR interferometry (D-InSAR) process was applied. The experimental results showed that two sites in the north-eastern part of the city exhibit a clear indication of land deformation. For a further evaluation of the D-InSAR result, the Persistent Scatterer (PS) and Small Baseline Subset (SBAS)-InSAR techniques were applied for 21 time series Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) data from 2003 to 2010. The comparison between the D-InSAR and SBAS-InSAR measurements had better agreement than that from the PS-InSAR measurement. The maximum deformation rate attributed to subsurface water injection for the period from 2003 to 2010 was up to approximately 33 mm/year in the line of sight (LOS) direction. The interferometric phase change from November 2007 to June 2010 showed a clear deformation pattern, and the rebound center has been expanding in scale and increasing in quantity.

Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy.

© 2017, Fuji Technology Press. All rights reserved. An earthquake (Mw6.2) struck Kumamoto Prefecture, Japan on April 14, 2016. A larger event (Mw7.0) struck the same area 28 hours later, on April 16. The series of earthquakes caused significant damage to buildings and infrastructures. Remote sensing is an effective tool to grasp damage situation over wide areas after a disaster strikes. In this study, two sets of ALOS-2 PALSAR-2 images taken before and after the earthquake were used to extract the areas with collapsed buildings. Three representative change indices, the co-event coherence, the ratio between the co- and pre-event coherence, and the z-factor combining the difference and correlation coefficients, were adopted to extract the collapsed buildings in the central district of Mashiki Town, the most severely affected area. The results of a building-by-building damage survey in the target area were used to investigate the most suitable threshold value for each index. The extracted results were evaluated by comparing them with the reference data from field surveys. Finally, the most valid factor was applied to larger affected areas for Kumamoto City and its surroundings.

The spatial distribution of the coseismic displacements that occurred along the Futagawa fault during the 2016 Kumamoto earthquake of M-w 7.0 was estimated using airborne light detection and ranging (lidar) data. In this study, a pair of digital surface models (DSMs) obtained from the high-density lidar data before and after the mainshock on 16 April 2016 were used. A window matching search approach based on the correlation coefficient between the two DSMs was used to estimate the geodetic displacement in the near-field region. The results showed good agreements with the geodetic displacements calculated from strong-motion acceleration records and coincided with the fault line surveyed by the Geological Survey of Japan.

<p>2011年東北地方太平洋沖地震（Mw9.0）の発生後，津波が繰り返し来襲したことにより多数の橋梁が甚大な被害を受けた．これによって道路網が寸断され，現地調査による早期の被害状況把握が困難となった．このような際，衛星画像は広範囲にわたる被害状況を現地に赴くことなく把握できる点から有用である．とくに合成開口レーダ（SAR）画像は雲や火煙の影響を受けず，昼夜撮影可能であるため緊急対応に適している．本研究では，津波により広範囲が被災した宮城県の沿岸部を対象地域として，発災前後の高分解能SAR画像を用いて，特定の橋梁領域に対して2時期の相関係数に閾値を設定し，被害有無の判別を試みた．その結果を被害報告書における橋梁の被災状況と比較して，本手法の精度と有用性を検討した．</p>

In this study, we tracked and analyzed the reconstruction process in Bam, Iran, after the city was struck by an earthquake with a M (w) of 6.6 on December 26, 2003. We adopted three approaches to comprehensively assess the city's post-earthquake reconstruction and to shed light on the progress and sustainability of disaster recovery projects. We applied the following methodology. First, we obtained official statistics and reports that included quantitative and qualitative evaluations of the reconstruction process to evaluate the overall outcome of the government's reconstruction projects. Second, we examined photographs taken during field surveys conducted in 2004, 2007, and 2014 to assess changes within the city. Last, we analyzed three satellite images of Bam-the first taken 3 months before the earthquake, the second immediately after the earthquake, and the third 8 years after the earthquake-to assess the progress of reconstruction work and changes in land cover and land use. The results indicated that considerable progress had been made in reconstructing some of the damaged areas. However, progress was relatively slow in severely damaged areas.

This study examines a novel extraction method for SAR imagery data of widespread flooding, particularly in the Chao Phraya river basin of central Thailand, where flooding occurs almost every year. Because the 2011 flood was among the largest events and of a long duration, a large number of satellites observed it, and imagery data are available. At that time, RADARSAT-2 data were mainly used to extract the affected areas by the Thai government, whereas ThaiChote-1 imagery data were also used as optical supporting data. In this study, the same data were also employed in a somewhat different and more detailed manner. Multi-temporal dual-polarized RADARSAT-2 images were used to classify water areas using a clustering-based thresholding technique, neighboring valley-emphasis, to establish an automated extraction system. The novel technique has been proposed to improve classification speed and efficiency. This technique selects specific water references throughout the study area to estimate local threshold values and then averages them by an area weight to obtain the threshold value for the entire area. The extracted results were validated using high-resolution optical images from the GeoEye-1 and ThaiChote-1 satellites and water elevation data from gaging stations.

This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single-and multi-temporal X-and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however, indicate that given dynamic parameter tuning, feature selection and an appropriate sampling approach, already small training samples (100 samples per class) are sufficient to produce high change detection rates. Moreover, the experiments show a good generalization ability of SVM which allows transfer and reuse of trained learning machines.