Robust CNNs for detecting collapsed buildings with crowd-sourced data

2019 ◽  
Author(s):  
Matthew J. Gibson ◽  
Dhruv Kaushik ◽  
Arcot Sowmya
Keyword(s):  
Collapsed Buildings

scholarly journals Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1146
Author(s):  
Yuliang Nie ◽  
Qiming Zeng ◽  
Haizhen Zhang ◽  
Qing Wang
Keyword(s):  
Damage Detection ◽  
Texture Features ◽  
Classification Performance ◽  
Building Damage ◽  
Alos Palsar ◽  
Yushu Earthquake ◽  
Matching Algorithm ◽  
2011 Tohoku Tsunami ◽  
Collapsed Buildings ◽  
Study Sites

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

Development and analysis of a bio-inspired wire-driven variable stiffness double spring based tapered multi-section flexible robot

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alok Ranjan Sahoo ◽  
Pavan Chakraborty
Keyword(s):  
Inverse Kinematics ◽  
Kinematic Model ◽  
Variable Stiffness ◽  
Radius Of Curvature ◽  
Flexible Robot ◽  
Content Type ◽  
Cluttered Environments ◽  
Collapsed Buildings ◽  
Practical Implications ◽  
Robot Body

Purpose The purpose of this paper is to develop a tendon actuated variable stiffness double spring based continuously tapered multi-section flexible robot and study its capability to achieve the desired bending and compression for inspection in cluttered environments. Design/methodology/approach Spring-based continuum manipulators get compressed while actuated for bending. This property can be used for the advantage in cluttered environments if one is able to control both bending and compression. Here, this paper uses a mechanics based model to achieve the desired bending and compression. Moreover, this study tries to incorporate the tapered design to help in independent actuation of the distal sections with minimal effects on proximal sections. This study is also trying to incorporate the double spring based design to minimize the number of spacers in the robot body. Findings The model was able to produce desired curvature at the tip section with less than 4.62% error. The positioning error of the manipulator is nearly 3.5% which is at par with the state-of-the-art manipulators for search and rescue operations. It was also found that the use of double spring can effectively reduce the number of spacers required. It can be helpful in smooth robot to outer world interaction without any kink. From the experiments, it has been found that the error of the kinematic model decreases as one moves from high radius of curvature to low radius of curvature. Error is maximum when the radius of curvature is infinity. Practical implications The proposed manipulator can be used for search operations in cluttered environments such as collapsed buildings and maintenance of heavy machineries in industries. Originality/value The novelty of this paper lies in the design and the proposed kinematics inverse kinematics for a spring-based continuously tapered multi-section manipulator.

scholarly journals Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

2018 ◽  
Vol 10 (11) ◽  
pp. 1689 ◽  
Author(s):  
Min Ji ◽  
Lanfa Liu ◽  
Manfred Buchroithner
Keyword(s):  
Satellite Imagery ◽  
Human Life ◽  
Haiti Earthquake ◽  
Minority Class ◽  
Imbalance Problem ◽  
Kappa Value ◽  
Under Sampling ◽  
Collapsed Buildings ◽  
Imbalance Learning ◽  
The Cost

Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.

scholarly journals INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 573-582 ◽  
Author(s):  
S. M. Tilon ◽  
F. Nex ◽  
D. Duarte ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Essential Information ◽  
Urban Scenes ◽  
Adversarial Network ◽  
Collapsed Buildings ◽  
Post Disaster

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

2020 ◽  
Vol 36 (1) ◽  
pp. 209-231
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Buildings ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

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.

Statistical Analysis of Japan Wood Frame Building Earthquake Debris Extent and Its Use in Road Networks in Japan

2019 ◽  
pp. 875529301987818
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Building ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

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 paper 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 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 (April 16, 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.

scholarly journals Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1924 ◽  
Author(s):  
Hiroyuki Miura ◽  
Tomohiro Aridome ◽  
Masashi Matsuoka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Building Damage ◽  
Aerial Images ◽  
Automated Identification ◽  
Damage Data ◽  
Collapsed Buildings ◽  
Post Disaster ◽  
Earthquake Data

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.

The role of materials science after the Mexico City earthquakes in 1985

1990 ◽  
Vol 5 (3) ◽  
pp. 658-665 ◽  
Author(s):  
V. Castaño ◽  
L. Martinez
Keyword(s):  
Mexico City ◽  
Materials Science ◽  
Hsla Steel ◽  
Construction Materials ◽  
Reinforcing Steel ◽  
Reinforcing Bars ◽  
Field Investigations ◽  
Earthquake Resistant ◽  
Collapsed Buildings

We review our field investigations of construction materials which were initiated after the 1985 Mexico City earthquakes. We report observations on reinforcing steel samples collected in the ruins of collapsed buildings and describe the experiences in the production and testing of HSLA steel reinforcing bars with mechanical and metallurgical properties suitable for earthquake resistant construction. We review some aspects of the cement and concrete industries of Mexico before 1985 and present a description of the properties of polymer modified cements considering the potential not only for construction but for many other applications.

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