scholarly journals Earthquakes Reconnaissance Data Sources, a Literature Review

2021 ◽  
Author(s):  
Diana Maria Contreras Mojica ◽  
Sean Wilkinson ◽  
Philip James
Keyword(s):  
Laser Scanning ◽  
Building Damage ◽  
Current Data ◽  
Data Sources ◽  
Damage Data ◽  
Efficient Data ◽  
Data Source ◽  
Single Data ◽  
Post Disaster ◽  
Selection Of

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and guide more efficient data. This paper reviews 38 articles that indicate the sources used by different authors to collect data related to damages and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria. Nowadays, reconnaissance mission can not rely on a single data source, and different data sources should complement each other, validate collected data, or quantify the damage comprehensively. The recent increase in the number of crowdsourcing and SM platforms as a source of data for earthquake reconnaissance is a clear indication of the tendency of data sources in the future.

scholarly journals Earthquake Reconnaissance Data Sources, a Literature Review

Earth ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 1006-1037
Author(s):  
Diana Contreras ◽  
Sean Wilkinson ◽  
Philip James
Keyword(s):  
Data Collection ◽  
Laser Scanning ◽  
Building Damage ◽  
Current Data ◽  
Data Sources ◽  
Efficient Data ◽  
Data Source ◽  
Single Data ◽  
Post Disaster ◽  
Selection Of

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source.

scholarly journals Earthquakes Reconnaissance Data Sources, a Literature Review

2021 ◽  
Author(s):  
Diana Maria Contreras Mojica ◽  
Sean Wilkinson ◽  
Philip James
Keyword(s):  
Data Collection ◽  
Laser Scanning ◽  
Building Damage ◽  
Current Data ◽  
Data Sources ◽  
Damage Data ◽  
Efficient Data ◽  
Data Source ◽  
Single Data ◽  
Selection Of

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

Event detection of different English data sources based on transfer learning

2021 ◽  
pp. 1-11
Author(s):  
Yanan Huang ◽  
Yuji Miao ◽  
Zhenjing Da
Keyword(s):  
Transfer Learning ◽  
Event Detection ◽  
Visual Analysis ◽  
Learning Algorithm ◽  
Data Sources ◽  
Data Set ◽  
Data Source ◽  
Single Data Source ◽  
The Difference ◽  
Single Data

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

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.

scholarly journals Metafora Konseptual Cinta dalam Lirik Lagu Taylor Swift di Album 1989

SUAR BETANG ◽  
2021 ◽  
Vol 16 (1) ◽  
Author(s):  
NFN Irwansyah
Keyword(s):  
Qualitative Method ◽  
Data Sources ◽  
Conceptual Metaphors ◽  
National Anthem ◽  
Personal Experiences ◽  
Data Source ◽  
Song Lyrics ◽  
Selection Of ◽  
Descriptive Qualitative

This study describes the conceptual metaphors of love in the lyrics of Taylor Swift's 1989 album. The purpose of this study is to describe the use of metaphors and conceptualize love-themed data. This research uses descriptive qualitative method with proficient free listening technique by taking song lyrics from Taylor Swift. The data source is song lyrics in Taylor Swift's 1989 album. The selection of the data sources is motivated by the fact that Taylor Swift is a singer who produces songs based on personal experiences. This makes the puns for the song more beautiful and poetic. The results show that the conceptualization of love metaphors found on Taylor Swift's 1989 album namely (1) love is a game, (2) love is fire, (3) heartbreak is the national anthem, (4) love is the object of trouble, (5) love is the throne, (6) love as glassware, (7) love as an object of color, (8) love is journey, (9) love is a sin, (10) love as a ship object, (11) love is life, (12) love is a trap, (13) love as an object falls, (14) love as an intoxicating object, (15) love is power and (16)  love as an object is hunted.AbstrakPenelitian ini mendeskripsikan metafora konseptual cinta dalam lirik lagu Taylor Swift di album 1989. Tujuan dari penelitian ini adalah untuk mendeskripsikan penggunaan metafora dan mengonseptualisasikan data yang bertema cinta. Penelitian ini menggunakan metode kualitatif deskriptif dengan teknik simak bebas libat cakap dengan mengambil lirik lagu dari Taylor Swift. Sumber data yang digunakan ialah lirik-lirik lagu pada album 1989 milik Taylor Swift. Pemilihan sumber data dilatarbelakangi bahwa Taylor Swift seorang penyanyi yang menghasilkan lagu berdasarkan pengalaman pribadi bersama pasangannya. Hal ini memunculkan permainan kata-kata yang terkesan lebih indah dan puitis. Hasil identifikasi menunjukkan konseptualisasi metafora cinta yang ditemukan pada album 1989 milik Taylor Swift antara lain (1) cinta adalah permainan, (2) cinta adalah api, (3) patah hati adalah lagu kebangsaan, (4) cinta sebagai objek masalah, (5) cinta adalah takhta, (6) cinta sebagai barang pecah belah, (7) cinta sebagai objek warna, (8) cinta adalah perjalanan, (9) cinta sebagai perbuatan dosa, (10) cinta sebagai objek kapal, (11) cinta adalah kehidupan, (12) cinta adalah perangkap, (13) cinta sebagai objek yang jatuh, (14) cinta sebagai objek yang memabukkan, (15) cinta adalah kekuatan, dan (16) cinta sebagai objek yang diburu.

scholarly journals Comparison of bibliographic data sources: Implications for the robustness of university rankings

2020 ◽  
pp. 1-34 ◽  
Author(s):  
Chun-Kai (Karl) Huang ◽  
Cameron Neylon ◽  
Chloe Brookes-Kenworthy ◽  
Richard Hosking ◽  
Lucy Montgomery ◽  
...  
Keyword(s):  
Citation Count ◽  
Data Sources ◽  
University Rankings ◽  
Multiple Sources ◽  
Data Set ◽  
Bibliographic Data ◽  
Data Source ◽  
English Speaking ◽  
International University ◽  
Single Data

Universities are increasingly evaluated on the basis of their outputs. These are often converted to simple and contested rankings with substantial implications for recruitment, income, and perceived prestige. Such evaluation usually relies on a single data source to define the set of outputs for a university. However, few studies have explored differences across data sources and their implications for metrics and rankings at the institutional scale. We address this gap by performing detailed bibliographic comparisons between Web of Science (WoS), Scopus, and Microsoft Academic (MSA) at the institutional level and supplement this with a manual analysis of 15 universities. We further construct two simple rankings based on citation count and open access status. Our results show that there are significant differences across databases. These differences contribute to drastic changes in rank positions of universities, which are most prevalent for non-English-speaking universities and those outside the top positions in international university rankings. Overall, MSA has greater coverage than Scopus and WoS, but with less complete affiliation metadata. We suggest that robust evaluation measures need to consider the effect of choice of data sources and recommend an approach where data from multiple sources is integrated to provide a more robust data set.

Exploring sensor data for agricultural statistics: The fruit is not hanging as low as we thought

2021 ◽  
pp. 1-14
Author(s):  
Ger Snijkers ◽  
Tim Punt ◽  
Sofie De Broe ◽  
José Gómez Pérez
Keyword(s):  
Business Processes ◽  
Current Data ◽  
Data Driven ◽  
Sensor Data ◽  
Small Scale ◽  
Use Case ◽  
New Methods ◽  
New Business ◽  
Data Source ◽  
Selection Of

New business processes are increasingly data driven as sensors have become ubiquitous. Sensor data could be a valuable new data source for official statistics. To study this presumption Statistics Netherlands conducted a small-scale use case in the area of agricultural statistics in collaboration with an innovative farmer. A selection of his sensor data was explored for overlap with current data demands in surveys. The aim of the study was to obtain insights in the available agricultural data, their data structure and quality, and explore new methods of data collection for agricultural statistics. The conclusion is that these data are valuable for replacing or pre-filling (parts of) certain agricultural surveys. However, many more challenges surfaced than we expected, to which the title of this paper refers. These challenges will be discussed in this paper.

Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Ping Yi ◽  
Songling Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Fusion ◽  
Incomplete Information ◽  
Data Sources ◽  
Fusion Technique ◽  
Crowd Management ◽  
Multiple Data ◽  
Data Source ◽  
Single Data

This paper introduces applications of the Dempster–Shafer (D-S) data fusion technique in transportation system decision making. D-S inference is a statistics-based data classification technique, and it can be used when data sources contribute discontinuous and incomplete information and no single data source can produce an overwhelmingly high probability of certainty for identifying the most probable event. The technique captures and combines the information contributed by the data sources by using Dempster’s rule to find the conjunction of the events and to determine the highest associated probability. The D-S theory is explained and its implementation described through numerical examples of a ride-hauling service and of crowd management at a subway station. Results from the applications have shown that the technique is very effective in dealing with incomplete information and multiple data sources in the era of big data.

scholarly journals Quantifying the Dynamics of Migration after a Disaster: Impact of Hurricane Maria in Puerto Rico

2020 ◽  
Author(s):  
Rolando J. Acosta ◽  
Nishant Kishore ◽  
Rafael A. Irizarry ◽  
Caroline Buckee
Keyword(s):  
Social Media ◽  
Puerto Rico ◽  
Census Data ◽  
Air Travel ◽  
Data Sources ◽  
Urban Centers ◽  
Population Loss ◽  
Data Source ◽  
Post Disaster ◽  
Wide Population

AbstractPopulation displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal post-disaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources to population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period, however, the magnitude and dynamics differ by data source. Census data predict a population loss of just over 129,000 from July 17 to July 2018, a 4% decrease; air travel data predicts a population loss of 168,295 for the same period of time, a 5% decrease; mobile phone based estimates predicts a loss of 235,375 form July 2017 to May 2018, an 8% decrease; and social media based estimates predict a loss of 476,779 from August 2017 to August 2018; a 17% decrease. On average, municipalities with smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island.

scholarly journals Comparing Google Maps and Uber Movement Travel Time Data

10.32866/5115 ◽  
2018 ◽  
Author(s):  
Hao Wu
Keyword(s):  
Travel Time ◽  
Data Sources ◽  
Research Subjects ◽  
Time Data ◽  
Google Maps ◽  
Travel Time Data ◽  
Data Source ◽  
Different Sources ◽  
Selection Of ◽  
Sydney Australia

The emergence and availability of crowd sourced data provide transport researchers with comprehensive coverage in their research subjects. However, difficulties in data validation and consistency between different sources pose a threat to the credibility of research based on such data. In this paper, travel time data for Sydney, Australia from Google Maps and from Uber Movement are compared for their consistency. Although the results show the two data sources are similar in measuring travel time, travel times from Uber Movement are systematically lower than from Google. This study recommends due caution in the selection of data source, and in comparing research results using different data sources.

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