Crowd-Assisted Disaster Scene Assessment with Human-AI Interactive Attention
Crowd-Assisted Disaster Scene Assessment with Human-AI Interactive Attention
复制标题
具有人机交互关注的人群辅助灾难场景评估
DOI：
10.1609/aaai.v34i03.5658
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Dong Wang
中科院分区：
文献类型：
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作者：
D. Zhang;Yifeng Huang;Yang Zhang;Dong Wang
移动的传感和人工智能（AI）的最新进展为灾害响应应用带来了新的革命。其中一个例子是灾害现场评估（DSA），它利用计算机视觉技术从目击者在社交媒体上提供的图像中评估灾害事件的损害严重程度。评估结果对于确定应急小组救援行动的优先次序至关重要。虽然AI算法可以显着减少此类应用中的检测时间和手动标记成本，但它们的性能往往达不到所需的准确性。我们的工作是由众包平台的出现（例如，Amazon Mechanic Turk，Waze），为人工智能应用程序获取人类智能提供了前所未有的机会。在本文中，我们开发了一种交互式灾难现场评估（iDSA）方案，该方案允许AI算法直接与人类交互，以识别DSA应用中灾难图像的显著区域。我们还开发新的激励设计和主动学习技术，以确保众包平台提供可靠、及时和具有成本效益的响应。我们对尼泊尔和厄瓜多尔地震事件中真实案例研究的评估结果表明，iDSA在准确评估灾害现场损失方面的表现明显优于最先进的基线。
The recent advances of mobile sensing and artificial intelligence (AI) have brought new revolutions in disaster response applications. One example is disaster scene assessment (DSA) which leverages computer vision techniques to assess the level of damage severity of the disaster events from images provided by eyewitnesses on social media. The assessment results are critical in prioritizing the rescue operations of the response teams. While AI algorithms can significantly reduce the detection time and manual labeling cost in such applications, their performance often falls short of the desired accuracy. Our work is motivated by the emergence of crowdsourcing platforms (e.g., Amazon Mechanic Turk, Waze) that provide unprecedented opportunities for acquiring human intelligence for AI applications. In this paper, we develop an interactive Disaster Scene Assessment (iDSA) scheme that allows AI algorithms to directly interact with humans to identify the salient regions of the disaster images in DSA applications. We also develop new incentive designs and active learning techniques to ensure reliable, timely, and cost-efficient responses from the crowdsourcing platforms. Our evaluation results on real-world case studies during Nepal and Ecuador earthquake events demonstrate that iDSA can significantly outperform state-of-the-art baselines in accurately assessing the damage of disaster scenes.