Localizing and Quantifying Damage in Social Media Images
Localizing and Quantifying Damage in Social Media Images
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DOI：
10.1109/asonam.2018.8508298
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Xukun Li;Xukun Li;Doina Caragea;Muhammad Imran
中科院分区：
文献类型：
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作者：
Xukun Li;Xukun Li;Doina Caragea;Muhammad Imran
传统的灾后损失评估严重依赖昂贵的GIS数据，特别是遥感图像数据。近年来，社交媒体已成为一个丰富的灾害信息来源，可能有助于以较低的成本评估损失。这种信息包括文本（例如，推特）或灾难目击者发布的图像。大多数现有的研究探讨了使用文本来识别对灾害响应团队有用的态势感知信息。使用社交媒体图像评估灾害损失的情况有限。在本文中，我们提出了一种新的方法，基于卷积神经网络和类激活映射，定位灾害图像中的损坏，并量化损坏的程度。我们提出的方法使社会网络图像的灾后损失评估的使用，并提供了一个廉价和可行的替代更昂贵的GIS方法。
Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment, and provides an inexpensive and feasible alternative to the more expensive GIS approach.