Satellite Data Analytics for Natural Disaster Assessment and Application to Pipeline Safety

2018 ◽  
Author(s):  
Mark Piazza ◽  
Karineh Gregorian ◽  
Gillian Robert ◽  
Nicolas Svacina ◽  
Lesley Gamble
Keyword(s):  
Change Detection ◽  
Natural Disasters ◽  
Natural Disaster ◽  
Satellite Data ◽  
The United States ◽  
Pipeline System ◽  
Detection Algorithms ◽  
Satellite Technology ◽  
Disaster Assessment ◽  
Change Detection Algorithms

Understanding where, when, and how conditions are changing along the extent of an energy pipeline system, which can be vast, is a challenging task. The challenge can be even greater when natural disasters1 create a condition where access to affected pipelines, qualified personnel, and equipment is limited. To address these challenges, pipeline operators are working directly with experts in satellite technology to develop innovative applications incorporating the use of satellite technology and analytical processes to improve natural disaster monitoring and response. Through recent experiences following Hurricane Harvey in the Gulf Coast region of the United States in August-September 2017 and the wildfires and mudslides in Southern California that occurred in December 2017 to January 2018, space-borne Synthetic Aperture Radar (SAR) satellite data was shown to be a useful tool for wide-area monitoring. Satellite-based SAR imagery has the unique advantage of penetrating through cloud cover and smoke and is capable of providing an early view of the extent of damage in both conditions. Satellite data and continuous improvements to their derived analytical products have resulted in significant benefits for pipeline operators preparing for and responding to the effects of potentially damaging natural processes, including river scour, erosion, avulsion, mudslides, and other threats to pipeline integrity and public safety. SAR change detection algorithms and processes can provide effective results in identifying areas affected by natural disasters that are not readily available by other means. These methods also provide timely information for allocating and directing resources to the most critical locations in support of post-disaster assessment and analysis. SAR satellite data and Amplitude Change Detection (ACD) algorithms provided the basis for confirming where flooding near pipeline infrastructure was most substantial following Hurricane Harvey. In the case of the Southern Californian forest fires and mudslides in Ventura and Santa Barbara counties, recent investigations into ACD and Coherence Change Detection (CCD) algorithms showed promising results, providing a detailed view of damaged areas in near-real time. This paper describes the process of collecting, analyzing, and applying satellite data for assessing the impacts of natural disasters on pipeline infrastructure, and the methods applied, consisting primarily of multiple change detection algorithms, that are used to process the large volume of satellite archive images to extract relevant changes. This paper also describes how these tools and products were practically applied to support decisions by pipeline operators to protect and ensure the integrity and safety of pipelines in the affected areas.

Towards Rapid Citywide Damage Mapping Using Neighborhood Edge Dissimilarities in Very High-Resolution Optical Satellite Imagery—Application to the 2003 Bam, Iran, Earthquake

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 255-266 ◽  
Author(s):  
Charles K. Huyck ◽  
Beverley J. Adams ◽  
Sungbin Cho ◽  
Hung-Chi Chung ◽  
Ronald T. Eguchi
Keyword(s):  
Assessment Tool ◽  
The United States ◽  
Building Damage ◽  
Urban Environments ◽  
Detection Algorithms ◽  
Sensing Technology ◽  
Change Detection Algorithms ◽  
Damage Mapping ◽  
Relief Efforts ◽  
Very High

Remote sensing technology is increasingly recognized as a valuable post-earthquake damage assessment tool. Recent studies performed by research teams in the United States, Japan, and Europe have demonstrated that building damage sustained in urban environments can be identified through analysis of optical imagery and synthetic aperture radar (SAR) data. Damage detection using automated change detection algorithms will soon facilitate the scaling and prioritization of relief efforts, as well as the monitoring of the recovery operations. This paper introduces the use of an edge dissimilarity algorithm to quantify the extent of building damage.

An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

2021 ◽  
Author(s):  
Gulnaz Alimjan ◽  
Yiliyaer Jiaermuhamaiti ◽  
Huxidan Jumahong ◽  
Shuangling Zhu ◽  
Pazilat Nurmamat
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Convolutional Network ◽  
Detection Algorithms ◽  
Multiple Dimensions ◽  
Change Detection Algorithms ◽  
Image Change Detection ◽  
Control Loss ◽  
The Relationship ◽  
Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

A CFAR optimization for low frequency UWB SAR change detection algorithms

2017 ◽  
Author(s):  
Ana C. F. Fabrin ◽  
Ricardo D. Molin ◽  
Dimas I. Alves ◽  
Renato Machado ◽  
Fabio M. Bayer ◽  
...  
Keyword(s):  
Change Detection ◽  
Low Frequency ◽  
Detection Algorithms ◽  
Change Detection Algorithms
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