A Study on the Development of Automated Damage Estimation System using High Resolution Satellite Imagery
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Abstract:
현재 우리나라에서는 자연재해로 인한 피해규모 파악을 위해 재해지역에 지자체 인력을 파견하여 수 작업에 의한 피해조사를 수행하고 있다. 그러나 한정된 인력과 수작업에 의한 피해조사로 많은 시간이 소요되어 어려움이 많은 실정이다. 따라서 본 연구에서는 태풍으로 인한 홍수 피해조사를 효율적으로 지원하기 위하여 피해규모 자동산정시스템을 개발하였다. 이를 위하여 영상기반의 변화탐지에 주로 활용되는 CVA(Change Vector Analysis)와 DNDVI(Differential Normalized Difference Vegetation Index) 알고리즘을 위성영상에 적용하여 피해면적을 산출하고, GIS DB를 이용하여 피해추정액을 산정하였다. 연구대상지역은 2013년 재해 피해가 컸던 여주시 흥천면으로 선정하였으며, 대상지역의 위성영상과 GIS DB를 구축하였다. 개발된 시스템을 대상지역 내에 피해가 심한 4개 지역에 적용하여 피해면적과 피해액을 산정하였다. 이후 동일지역에 대해 육안판독을 수행하여 피해면적과 피해액을 산정하고 이를 참값으로 가정하였다. 시스템을 통해 산정된 피해면적과 피해액을 참값과 비교한 결과, 피해면적의 분류정확도는 87.23%, Kappa 값은 0.716으로 나타났다. 또한, 4개 지역에 대한 육안판독 결과, 산정된 피해액의 규모가 시스템의 결과와 동일한 순서로 나타났다. 따라서 개발된 시스템을 피해조사를 위한 우선순위 선정과 인력 배분 결정 등에 활용한다면 현장조사에 의존하는 기존 피해조사 업무 효율성의 제고에 기여가 클 것으로 사료된다. Currently in South Korea, to grasp the damage of natural disasters, damage investigations are conducted by dispatching personnel from local governments to disaster regions. However, there are difficulties due to limited human resources and damage investigations that must be done manually and such investigations are considerably to be time consuming. Therefore, to provide support the investigations of flood damage due to typhoons, this study developed an automated damage estimation system. To achieve this goal, CVA(Change Vector Analysis) and DNDVI(Differential Normalized Difference Vegetation) algorithms were used to calculate the damage areas and estimated the damage cost using GIS DB. Heungcheon-myeon in Yeoju-si was selected as the study area and the database was constructed using satellite images and GIS DB in the study area. To calculate the size of the damage areas and damage cost, the developed system was applied to four sites with serious damages. The true value of the size of the damage areas and the damage cost were estimated through the visual interpretation of the damage areas. When the damaged areas and the damage cost calculated through the system were compared to the true values, the classification accuracy of the damage areas was 87.23% and the Kappa value was 0.716. Furthermore, according to the results of visual interpretation of the four sites, the rank of the calculated damage cost was of the same order as that of the results derived from the system. Consequently, if the developed system is used to determine priorities and allocation of personnel for damage investigations, it will contribute to enhance the work efficiency of the existing damage investigations.Keywords:
Typhoon
Natural hazard
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The purpose of this research is to identify the indicators of typhoon damage and develop a metric for typhoon vulnerability functions employing the losses associated with Typhoon Maemi. Typhoons cause significant financial damages worldwide every year. Federal and local governments, insurance companies, and construction companies strive to develop typhoon risk assessment models and use them to quantify the risks so that they can avoid, mitigate, or transfer the financial risks. Therefore, typhoon risk assessment modeling is becoming increasingly important, and in order to achieve a sophisticated evaluation, it is also important to reflect more specified and local vulnerabilities. Although several previous studies on economic loss associated with natural catastrophe have identified essential risk indicators, there has been a shortage of more specific research studies focusing on the correlation between vulnerability and economic loss caused by typhoons. In order to fill this gap, this study collected and analyzed the actual loss record of Typhoon Maemi collected and accumulated by a major insurance company in Korea. In order to create the vulnerability functions and to identify the natural hazard indicators and basic building information indicators, information from the insurance record was used in the analysis. The results and metric of this research provide a pragmatic approach that helps create vulnerability functions for abovementioned sectors and like estimating local vulnerabilities and predicting and coping with the possible damage and loss from typhoons.
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Natural hazard
Vulnerability
Economic shortage
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Intensity
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Tropical cyclone scales
Intensity
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Mixed layer
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