Real-time identification of disaster areas by an open-access vision-based tool

Balkaya, Can; Casciati, Fabio; Casciati, Sara; Faravelli, Lucia; Vece, Michele

doi:10.1016/j.advengsoft.2015.06.002

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…Disaster-affected areas require faster and efficient coverage so that help can be provided to people adequately [110]. Balkaya et al [111] emphasized the need for an infrastructure that can deliver real-time data about the disaster-affected zones so that damage estimation can be swift. The study noted that the most accurate solution will be the multi-viewpoint of image/video fusion based on data input from both satellites and ground stations.…”

Section: Computer Vision For Flood Modelling and Mappingmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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Section: Computer Vision For Flood Modelling and Mappingmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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“…Lee et al (2014) produziram um conjunto de dados de alta resolução de precipitação para subsidiar estudos hidrológicos e climatológicos em bacias hidrográficas com baixa disponibilidade de dados pluviométricos. O QGIS também é amplamente utilizado na gestão de catástrofes, desastres naturais e respostas humanitárias (Balkaya et al, 2015;Khalil et al, 2015;Leidig e Teeuw, 2015). Dile et al (2016) desenvolveram uma interface da ferramenta de modelagem de bacias hidrográficas, a The Soil and Water Assessment Tool (SWAT), para ser acoplada ao QGIS, denomina QSWAT.…”

Section: Exemplos De Utilização Do Qgis Nas Ciências Ambientaisunclassified

Aplicabilidade de Sistemas de Informações Geográficas (SIGs) livres nas ciências ambientais: o uso do QGIS

Bruno¹

2017

Rev. Bras. Gest. Amb. Sustent.

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Resumo. Os Sistemas de Informações Geográficas (SIGs) tornaramse componentes essenciais em variados campos de pesquisas e tarefas científicas dentro do domínio das ciências ambientais. Nesta categoria de aplicativos, destacam-se os chamados softwares livres por demostrarem potencialidades promissoras. Este trabalho abordou a utilização das geotecnologias livres com enfoque no uso do QGIS. Utilizou-se como ferramenta a revisão de literatura a partir de estudos relacionados às ciências ambientais. Conclui-se que o software livre pode ser considerado uma abrangente plataforma de processamento e divulgação do conhecimento.Palavras-chave: Ambiente; Recursos hídricos; Geoprocessamento. Abstract. Applicability of free Geographical Information Systems (GIS) in environmental sciences.Geographic information systems (GIS) have become essential components in a variety of research fields and scientific tasks within the field of environmental sciences. In this category of applications, stand out the so-called free software for demonstrating promising potentialities. This work addresses a use of free geotechnologies with a focus without QGIS. It was used as the literature review from studies related to the environmental sciences. It is concluded that free software can be considered a comprehensive platform for processing and disseminating knowledge.

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“…Researchers have proposed various means for rapidly identifying damaged structures in an affected area after an earthquake. Casciati and colleagues [10][11][12] conducted rapid damage assessment by comparing images from satellites of buildings and infrastructure at the pre-event stage and after the occurrence of the earthquake. Digital image processing techniques and geographic information system (GIS) integration were leveraged to conduct change detection.…”

Section: Introductionmentioning

confidence: 99%

Rapid postearthquake safety evaluation of buildings using sparse acceleration measurements

Tsuchimoto

Narazaki

Hoskere

et al. 2020

Structural Health Monitoring

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After a seismic event, buildings need to be inspected to confirm their safety prior to reoccupation. As such, the rapid evaluation of the condition of individual buildings is important for minimizing disruption to lives and business. However, traditional manual inspection by experts is laborious and time-consuming. Structural health monitoring provides the potential to accelerate the required evaluation. This article proposes a cost-effective approach for rapid safety evaluation of buildings after seismic events using sparse acceleration measurements. First, a damage-sensitive feature is defined that can be used to infer the condition of buildings. Herein, the maximum interstory drift angle is proposed as a reliable damage index to classify the safety of buildings after seismic events. A convolutional neural network is then employed to uncover the complex relationship between the damage-sensitive features and the building condition. A five-story steel building is considered to validate the proposed approach. First, a three-dimensional nonlinear model of the building is created. To generate the required training data, a simplified nonlinear model is developed, along with a corresponding linear model, as use of the three-dimensional model is too computationally expensive. The training data for the convolutional neural network incorporates uncertainties in both the analysis model and the ground motion. Initial evaluation is conducted using the simplified nonlinear model, while final validation of the proposed approach is performed using the results of the three-dimensional nonlinear analysis model subjected to historical earthquakes. The results demonstrate the ability of the proposed approach to accommodate differences between the in-situ structure and the analysis model, as well as the efficacy of this approach for rapid postearthquake safety evaluation of buildings.

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Real-time identification of disaster areas by an open-access vision-based tool

Cited by 18 publications

References 5 publications

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Aplicabilidade de Sistemas de Informações Geográficas (SIGs) livres nas ciências ambientais: o uso do QGIS

Rapid postearthquake safety evaluation of buildings using sparse acceleration measurements

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