Assessing Building Damage by Learning the Deep Feature Correspondence of Before and After Aerial Images

Gullies are sources and reservoirs of sediments and perform as efficient transfers of runoff and sediments. In recent years, several techniques and technologies emerged to facilitate monitoring of gully dynamics at unprecedented spatial and temporal resolutions. Here we present a detailed study of a valley-bottom gully in a Mediterranean rangeland with a savannah-like vegetation cover that was partially restored in 2017. Restoration activities included check dams (gabion weirs and fascines) and livestock exclosure by fencing. The specific objectives of this work were: (1) to analyze the effectiveness of the restoration activities, (2) to study erosion and deposition dynamics before and after the restoration activities using high-resolution digital elevation models (DEMs), (3) to examine the role of micro-morphology on the observed topographic changes, and (4) to compare the current and recent channel dynamics with previous studies conducted in the same study area through different methods and spatio-temporal scales, quantifying medium-term changes. Topographic changes were estimated using multi-temporal, high-resolution DEMs produced using structure-from-motion (SfM) photogrammetry and aerial images acquired by a fixed-wing unmanned aerial vehicle (UAV). The performance of the restoration activities was satisfactory to control gully erosion. Check dams were effective favoring sediment deposition and reducing lateral bank erosion. Livestock exclosure promoted the stabilization of bank headcuts. The implemented restoration measures increased notably sediment deposition.

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

Remote Sensing ◽

10.3390/rs12121924 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1924 ◽

Cited By ~ 7

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.

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Keyframe-Based Vehicle Surveillance Video Retrieval

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2018100104 ◽

2018 ◽

Vol 10 (4) ◽

pp. 52-61

Author(s):

Xiaoxi Liu ◽

Ju Liu ◽

Lingchen Gu ◽

Yannan Ren

Keyword(s):

Video Retrieval ◽

Public Security ◽

Video Content ◽

Surveillance Video ◽

Forensic Evidence ◽

Improve Performance ◽

Retrieval Method ◽

Deep Feature ◽

Before And After ◽

Short Video

This article describes how due to the diversification of electronic equipment in public security forensics, vehicle surveillance video as a burgeoning way attracts us attention. The vehicle surveillance videos contain useful evidence, and video retrieval can help us find evidence contained in them. In order to get the evidence videos accurately and effectively, a convolution neural network (CNN) is widely applied to improve performance in surveillance video retrieval. In this article, it is proposed that a vehicle surveillance video retrieval method with deep feature derived from CNN and with iterative quantization (ITQ) encoding, when given any frame of a video, it can generate a short video which can be applied to public security forensics. Experiments show that the retrieved video can describe the video content before and after entering the keyframe directly and efficiently, and the final short video for an accident scene in the surveillance video can be regarded as forensic evidence.

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Semi-auto assessment system on building damage caused by landslide disaster with high-resolution satellite and aerial images

10.1117/12.2193733 ◽

2015 ◽

Cited By ~ 1

Author(s):

Bo Sun ◽

Qihua Xu ◽

Jun He ◽

Fengxiang Ge ◽

Ying Wang

Keyword(s):

High Resolution ◽

Building Damage ◽

Assessment System ◽

Aerial Images ◽

Landslide Disaster

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Adapted deep feature fusion for person re-identification in aerial images

Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything ◽

10.1117/12.2305090 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arne Schumann ◽

Jürgen Metzler

Keyword(s):

Feature Fusion ◽

Aerial Images ◽

Deep Feature

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The 2017 catastrophic subsidence in the Dålk Glacier: Unmanned aerial survey and digital terrain analysis

10.7287/peerj.preprints.27064 ◽

2018 ◽

Author(s):

Igor V Florinsky ◽

Dmitrii Bliakharskii ◽

Sergei Popov ◽

Sergei Pryakhin

Keyword(s):

Aerial Survey ◽

Probable Mechanism ◽

The Other ◽

Aerial Images ◽

Terrain Analysis ◽

Digital Terrain Analysis ◽

Glacier Surface ◽

Digital Terrain ◽

Before And After ◽

First Results

We present the first results from a study of the 2017 catastrophic subsidence in the Dålk Glacier, East Antarctica using an unmanned aerial system (UAS) and UAS-derived DEMs. The subsided portion of the Dålk Glacier and adjacent territory was surveyed in two flights, before and after the collapse. The survey was performed by Geoscan 201, a small flying-wing UAS. Aerial images have an average resolution of 6 cm. Using Agisoft PhotoScan software, we generated two DEMs with a resolution of 22 cm related to the pre- and post-collapsed glacier surface. To model the pre-collapsed subglacial cavern, one DEM was subtracted from the other. Finally, we discuss a probable mechanism of the catastrophic subsidence.

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Development of Earthquake-Induced Building Damage Estimation Model Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

Journal of Disaster Research ◽

10.20965/jdr.2013.p0346 ◽

2013 ◽

Vol 8 (2) ◽

pp. 346-355 ◽

Cited By ~ 19

Author(s):

Masashi Matsuoka ◽

◽

Miguel Estrada ◽

Keyword(s):

Discriminant Function ◽

Likelihood Function ◽

Window Size ◽

Seismic Intensity ◽

Building Damage ◽

Severe Damage ◽

Estimation Model ◽

Alos Palsar ◽

Explanatory Variables ◽

Before And After

With the aim of developing a model for estimating building damage from synthetic aperture radar (SAR) data in the L-band, which is appropriate for Peru, we propose a regression discriminant function based on field survey data in Pisco, which was seriously damaged in the 2007 Peru earthquake. The proposed function discriminates among damage ranks corresponding to the severe damage ratio of buildings using ALOS/PALSAR imagery of the disaster area before and after the earthquake. By calculating differences in and correlations of backscattering coefficients, which were explanatory variables of the regression discriminant function, we determined an optimum window size capable of estimating the degree of damage more accurately. A normalized likelihood function for the severe damage ratio was developed based on discriminant scores of the regression discriminant function. The distribution of the severe damage ratio was accurately estimated, furthermore, from PALSAR imagery using data integration of the likelihood function with fragility functions in terms of the seismic intensity of the earthquake.

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Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression

Remote Sensing ◽

10.3390/rs11232858 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2858 ◽

Cited By ~ 6

Author(s):

Tianyu Ci ◽

Zhen Liu ◽

Ying Wang

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Learning Algorithm ◽

Network Models ◽

Building Damage ◽

Ordinal Regression ◽

Aerial Images ◽

Deep Learning Algorithm ◽

Ordinal Regression Model

We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods.

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The influence of migratory birds on the distribution of the seagrass Zostera japonica

Botanica Marina ◽

10.1515/bot-2020-0045 ◽

2020 ◽

Vol 63 (6) ◽

pp. 521-525

Author(s):

Fumiyuki Sato ◽

Satoshi Tanaka ◽

Shinji Kirihara ◽

Yoshiyuki Tanaka

Keyword(s):

Migratory Birds ◽

Major Effect ◽

Grazing Pressure ◽

Aerial Images ◽

Zostera Japonica ◽

Branta Bernicla ◽

Before And After ◽

Migrating Birds ◽

Aomori Prefecture

AbstractGrazing pressure by animals can change the distribution and biomass of seagrass. We examined grazing pressure by conducting transect surveys and acquiring aerial images by drone before and after the arrival of migratory birds along the Asadokoro tide flats, Aomori Prefecture, Japan. The distribution and biomass of the seagrass Zostera japonica decreased sharply between October and November 2018, which was when migrating waterfowl arrived. We hypothesized that grazing pressure by migrating birds such as the Anatidae, including whooper swans (Cygnus cygnus) and brent geese (Branta bernicla), had a major effect on the decline in Z. japonica in late October. Shortly after the Anatidae arrived, most of the Z. japonica in the shallows disappeared, including the belowground parts. The abundance of Z. japonica in this area was insufficient to support wintering swans. Swans likely need food other than Z. japonica for overwintering.

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Post-War Building Damage Detection

Proceedings ◽

10.3390/ecrs-2-05172 ◽

2018 ◽

Vol 2 (7) ◽

pp. 359

Author(s):

Ali Ghandour ◽

Abedelkarim Jezzini

Keyword(s):

Damage Detection ◽

Critical Infrastructure ◽

Residential Buildings ◽

Accuracy Assessment ◽

Building Damage ◽

Aerial Images ◽

Reconstruction Process ◽

Size Type ◽

Post War ◽

Occurrence Matrix

Natural disasters and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process. Building damage detection due to war activities has not been thoroughly discussed in the literature. In this paper, an automated building damage detection technique that relies on both pre- and post-war aerial images is proposed. Building damage estimation was done using shadow information and Gray Level Co-occurrence Matrix features. Accuracy assessment applied over a Syrian war-affected zone near Damascus reveals the excellent performance of the proposed technique.

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