| 地震应急信息自动分类方法研究 |
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| 引用本文： | 王琳,姜立新,杨天青,张维佳.地震应急信息自动分类方法研究[J].震灾防御技术,2019,14(4):907-916. |
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| 作者姓名： | 王琳 姜立新 杨天青 张维佳 |
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| 作者单位： | 1.中国地震局地震预测研究所, 北京 100036 |
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| 基金项目： | 地震科技星火项目（1840807302）：川滇地区人员伤亡动态研究和国家重点研发计划、（2018YFC1504506）：基于云技术的地震应急产品与信息服务平台共同资助 |
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| 摘 要： | 地震应急信息的高效处理为地震应急救援工作提供了重要支撑。本文根据地震应急信息分类的需求，构建了一种高效便捷的地震信息分类处理方法。以震前、震时、震后为时间主线，将地震应急信息分为震前基础背景信息、地震震情灾情信息及震后应急救援信息，并采用“关键词分类”的方法，在计算机语言的支持下，将多渠道汇集的应急信息进行自动分类，在一定程度上缩短了应急信息加工处理与服务的时间，能快速高效地为应急指挥提供信息服务。
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| 关 键 词： | 地震 应急信息 自动分类 关键词分类 |
| 收稿时间： | 2018/12/11 0:00:00 |
Research on the Method of Automatic Classification in Earthquake Emergency Information |
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| Wang Lin,Jiang Lixin,Yang Tianqing and Zhang Weijia.Research on the Method of Automatic Classification in Earthquake Emergency Information[J].Technology for Earthquake Disaster Prevention,2019,14(4):907-916. |
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| Authors: | Wang Lin Jiang Lixin Yang Tianqing and Zhang Weijia |
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| Institution: | 1.Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China2.China Earthquake Networks Center, Beijing 100045, China |
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| Abstract: | In order to solve the problem that the automation degree of extracting damaged Buildings caused by earthquake is not very high, in this paper a fully convolutional neural network is applied to extract the remote sensing information of earthquake damage to buildings. The 0.2m-resolution aerial image of the Yushu County urban area obtained after the Yushu earthquake was used as the data source to test the result of convolutional neural network. The objects in the test area were classified into collapsed buildings, uncollapsed buildings, and background. Classify and label 427 sub-images of 500×500 pixels manually, 393 of them were selected as training sample set, and others as verification sample set. The training sample set is used to train the full convolutional neural network and the trained network is used to extract the building seismic damage information and evaluate the accuracy based on the verification sample. The result shows that the overall classification accuracy is 82.3%, and the fully convolutional neural network can improve the automation of information extraction and has a better ability to extract building seismic damage information. |
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| Keywords: | Earthquake Emergency information Automatic classification Keywords classification |
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