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1. On Identifying Hashtags in Disaster Twitter Data

   https://doi.org/10.1609/aaai.v34i01.5387  

   Ray Chowdhury, Jishnu; Caragea, Cornelia; Caragea, Doina (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1 

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2. Capacity-aware cost-efficient network reconstruction for post-disaster scenario

   https://doi.org/10.1109/PIMRC.2016.7794910  

   Wang, Xiaoyan; Zhou, Hao; Zhong, Lei; Ji, Yusheng; Takano, Kiyoshi; Yamada, Shigeki; Xue, Guoliang (September 2016, IEEE PIMRC)

   Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model. 

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3. Improve disaster communication in hyperlocal online and offline communities using social media data: A case study of the 2015 Nepal earthquake.

   Dozier, J.; McFarland, K; Ghanipoor Machiani, S; Nara, A; Yang, X.; Tsou, M.H. (January 2019, Proceedings in 2019 Annual Meeting of Transportation Research Board (TRB), January 13–17, Washington D.C.)

   This article seeks to go beyond traditional GIS methods used in creating maps for disaster response that commonly look at the disaster extent. Instead, a slightly different approach is taken using social media data collected from Twitter to explore how people communicate during disaster events, how online communities form and evolve, and how communication methods can improve. This study collected the Twitter data during the 2015 Nepal earthquake disaster and applied a spatiotemporal analysis to find any patterns that show shadows or gaps in communication channels in local communities’ communication. Linkages in social media can be used to understand how people communicate, how quickly they diffuse information, and how social networks form online during disasters. These can improve communication throughout disaster phases. This study offers a deeper understanding of the kinds of spatiotemporal patterns and spatial social networks that can be observed during disaster events. The need for better communication during disaster events is imperative for better disaster management, increasing community resilience, and saving lives. 

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4. Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

   https://doi.org/10.59297/FWXE4933  

   Zou, Henry Peng; Caragea, Cornelia; Zhou, Yue; and Caragea, Doina (May 2023, Proceedings of the 20th International ISCRAM Conference)

   Radianti, Jaziar; Dokas, Ioannis; Lalone, Nicolas; Khazanchi, Deepak (Ed.)

   The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results. 

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5. Damage Estimation and Localization from Sparse Aerial Imagery

   https://doi.org/10.1109/ICMLA52953.2021.00028  

   Franceschini, R.; Liu, J.; Amin, S. (December 2021, 20th IEEE International Conference on Machine Learning and Applications)

   Aerial images provide important situational aware- ness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As such, DEL from such imagery is still a highly manual and time-consuming process. We propose an approach to both detect damage in aerial images and localize it in world coordinates, with specific focus on detecting and localizing flooding. The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates. We evaluate the performance of our approach on post-event data from the 2016 Louisiana floods, and find that our approach achieves a precision of 88%. Given this high precision using limited data, we argue that this approach is currently viable for fast and effective DEL from handheld aerial imagery for disaster response. 

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