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.

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.

scholarly journals Intercomparison and validation of building damage assessments based on post-Haiti 2010 earthquake imagery using multi-source reference data

2013 ◽  
Vol 1 (2) ◽  
pp. 1445-1486 ◽  
Author(s):  
G. Lemoine ◽  
C. Corbane ◽  
C. Louvrier ◽  
M. Kauffmann
Keyword(s):  
Building Damage ◽  
Aerial Imagery ◽  
Urban Structures ◽  
Sensing Technology ◽  
Ground Survey ◽  
Damage Grade ◽  
Damage Extent ◽  
Very High ◽  
Haiti 2010 Earthquake

Abstract. The Haiti 2010 earthquake is one of the first major disasters in which very high resolution satellite and airborne imagery was embraced to delineate the event impact. Several rapid mapping initiatives exploited post-earthquake satellite and airborne imagery to produce independent point feature sets marking the damage grade of affected buildings. Despite the obvious potential of the satellite remote sensing technology in providing damage figures, the scale and complexity of the urban structures in Port-au-Prince cause overall figures and patterns of the damage assessments to yield a rather poor representation of the true damage extent. The higher detail airborne imagery performs much better as confirmed by different validation studies carried out in the last two years. In this paper, in addition to the review and analysis of the different validation works, we investigate the quality of damage assessment derived by different activities through a simple intercomparison and a validation using a complete building ground survey. The results show that the identification of building damage from aerial imagery provides a realistic estimate of the spatial pattern and intensity of building damage.

scholarly journals Development and validation of the Readiness to Train Assessment Tool (RTAT)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ianita Zlateva ◽  
Amanda Schiessl ◽  
Nashwa Khalid ◽  
Kerry Bamrick ◽  
Margaret Flinter
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Conceptual Framework ◽  
Health Center ◽  
Assessment Tool ◽  
Training Programs ◽  
Health Professions ◽  
Internal Consistency Reliability ◽  
The United States ◽  
Health Centers

Abstract Background In recent years, health centers in the United States have embraced the opportunity to train the next generation of health professionals. The uniqueness of the health centers as teaching settings emphasizes the need to determine if health professions training programs align with health center priorities and the nature of any adjustments that would be needed to successfully implement a training program. We sought to address this need by developing and validating a new survey that measures organizational readiness constructs important for the implementation of health professions training programs at health centers where the primary role of the organizations and individuals is healthcare delivery. Methods The study incorporated several methodological steps for developing and validating a measure for assessing health center readiness to engage with health professions programs. A conceptual framework was developed based on literature review and later validated by 20 experts in two focus groups. A survey-item pool was generated and mapped to the conceptual framework and further refined and validated by 13 experts in three modified Delphi rounds. The survey items were pilot-tested with 212 health center employees. The final survey structure was derived through exploratory factor analysis. The internal consistency reliability of the scale and subscales was evaluated using Chronbach’s alpha. Results The exploratory factor analysis revealed a 41-item, 7-subscale solution for the survey structure, with 72% of total variance explained. Cronbach’s alphas (.79–.97) indicated high internal consistency reliability. The survey measures: readiness to engage, evidence strength and quality of the health professions training program, relative advantage of the program, financial resources, additional resources, implementation team, and implementation plan. Conclusions The final survey, the Readiness to Train Assessment Tool (RTAT), is theoretically-based, valid and reliable. It provides an opportunity to evaluate health centers’ readiness to implement health professions programs. When followed with appropriate change strategies, the readiness evaluations could make the implementation of health professions training programs, and their spread across the United States, more efficient and cost-effective. While developed specifically for health centers, the survey may be useful to other healthcare organizations willing to assess their readiness to implement education and training programs.

CYSTIC FIBROSIS OF THE PANCREAS

PEDIATRICS ◽  
1957 ◽  
Vol 19 (6) ◽  
pp. 1136-1138
Author(s):  
Paul A. di Sant'Agnese ◽  
Charles Upton Lowe
Keyword(s):  
Cystic Fibrosis ◽  
Clinical Evidence ◽  
Clinical Effect ◽  
Single Gene ◽  
Pancreatic Insufficiency ◽  
Recessive Gene ◽  
The United States ◽  
Live Births ◽  
Almost All ◽  
Very High

IN THE COURSE of a review of all features of the disease, the following points were particularly noteworthy: Incidence This disease accounts for almost all cases of pancreatic insufficiency in children. The incidence in the population of the United States is between 1 in 600 and 1 in 10,000 live births, with a probable average incidence of 1 in 2,500. There is no sex predominance. There is, however, a difference in racial predilection, being rarely seen in the Negro and never in Mongolians. It is a familial disease, displaying the characteristics of a mendelian recessive gene. This means that in an affected family the disease may occur in approximately 25% of the offspring, that both parents must be carriers of the trait and that two-thirds of the non-affected children are also carriers. Birth order has no effect on the inheritance of this disease. The fact that it is usually a lethal disease indicates that the mutation rate for this gene must be very high; the frequency of the single gene in the population has been calculated to be approximately 1 in 50. Pancreatic Insufficiency Clinical evidence of poor digestion and absorption of protein and fat is seen in the increased quantities of these substances in the feces, which causes the feces to be bulky, foul smelling, foamy and greasy. Another clinical effect of malabsorption is seen in the failure of the newborn infant with cystic fibrosis of the pancreas to regain birth weight in the first 10 days of life. In the absence of other evidence of disease, this is a sign suggestive of pancreatic failure.

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.

Alstom Gas Turbine Technology Overview: Status 2014

2015 ◽  
Author(s):  
Wolfgang Kappis ◽  
Stefan Florjancic ◽  
Uwe Ruedel
Keyword(s):  
Power Generation ◽  
Gas Turbine ◽  
Gas Turbines ◽  
New Technologies ◽  
The United States ◽  
Heavy Duty ◽  
Fuel Flexibility ◽  
Market Requirements ◽  
Base Load ◽  
Very High

Market requirements for the heavy duty gas turbine power generation business have significantly changed over the last few years. With high gas prices in former times, all users have been mainly focusing on efficiency in addition to overall life cycle costs. Today individual countries see different requirements, which is easily explainable picking three typical trends. In the United States, with the exploitation of shale gas, gas prices are at a very low level. Hence, many gas turbines are used as base load engines, i.e. nearly constant loads for extended times. For these engines reliability is of main importance and efficiency somewhat less. In Japan gas prices are extremely high, and therefore the need for efficiency is significantly higher. Due to the challenge to partly replace nuclear plants, these engines as well are mainly intended for base load operation. In Europe, with the mid and long term carbon reduction strategy, heavy duty gas turbines is mainly used to compensate for intermittent renewable power generation. As a consequence, very high cyclic operation including fast and reliable start-up, very high loading gradients, including frequency response, and extended minimum and maximum operating ranges are required. Additionally, there are other features that are frequently requested. Fuel flexibility is a major demand, reaching from fuels of lower purity, i.e. with higher carbon (C2+), content up to possible combustion of gases generated by electrolysis (H2). Lifecycle optimization, as another important request, relies on new technologies for reconditioning, lifetime monitoring, and improved lifetime prediction methods. Out of Alstom’s recent research and development activities the following items are specifically addressed in this paper. Thermodynamic engine modelling and associated tasks are discussed, as well as the improvement and introduction of new operating concepts. Furthermore extended applications of design methodologies are shown. An additional focus is set ono improve emission behaviour understanding and increased fuel flexibility. Finally, some applications of the new technologies in Alstom products are given, indicating the focus on market requirements and customer care.

scholarly journals Object detection networks and augmented reality for cellular detection in fluorescence microscopy

2020 ◽  
Vol 219 (10) ◽  
Author(s):  
Dominic Waithe ◽  
Jill M. Brown ◽  
Katharina Reglinski ◽  
Isabel Diez-Sevilla ◽  
David Roberts ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Back Projection ◽  
Detection Algorithms ◽  
High Classification Accuracy ◽  
Fast Processing ◽  
Automate Detection ◽  
Very High

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

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