Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.

社交媒体帖子越来越多地用于现代灾害管理。与文本信息一起，社交媒体上发布的图像中固有的上下文和线索在确定对特定灾难的适当应急响应方面发挥着重要作用。在本文中，我们提出了应急响应的灾难分类法，并使用与应急响应管道相同的分类法以及基于深度学习的图像分类和对象识别算法来自动化应急响应决策过程。我们使用卡片分类法来验证灾难分类法的完整性和正确性。我们还使用 VGG-16 和 You Only Look Once (YOLO) 算法来分析与灾难相关的图像并识别灾难类型和相关线索（例如出现在这些图像中的对象）。此外，我们使用决策表和应用层次分析法 (AHP)，将中间输出对齐，以将与灾害相关的图像映射到灾害分类法中，并为给定的灾害确定适当的应急响应类型。所提出的方法已使用地震、飓风和台风作为用例进行了验证。结果表明，使用 YOLOv4 对 96% 的图像进行了灾难分类正确分类。使用增量训练方法可以进一步提高准确性。由于在图像分析中使用了基于云的深度学习算法，我们的方法可能对实时危机管理有用。这些算法以及所提出的应急响应管道可以通过从社交媒体上发布的多媒体信息中提取的其他时空特征进一步增强。