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1. Disaster Image Classification Using Pre-trained Transformer and Contrastive Learning Models

   https://doi.org/10.1109/DSAA60987.2023.10302517  

   Dinani, Soudabeh; Caragea, Doina (October 2023, The 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA))

   Natural disasters can have devastating consequences for communities, causing loss of life and significant economic damage. To mitigate these impacts, it is crucial to quickly and accurately identify situational awareness and actionable information useful for disaster relief and response organizations. In this paper, we study the use of advanced transformer and contrastive learning models for disaster image classification in a humanitarian context, with focus on state-of-the-art pre-trained vision transformers such as ViT, CSWin and a state-of-the-art pre-trained contrastive learning model, CLIP. We evaluate the performance of these models across various disaster scenarios, including in-domain and cross-domain settings, as well as few- shot learning and zero-shot learning settings. Our results show that the CLIP model outperforms the two transformer models (ViT and CSWin) and also ConvNeXts, a competitive CNN-based model resembling transformers, in all the settings. By improving the performance of disaster image classification, our work can contribute to the goal of reducing the number of deaths and economic losses caused by disasters, as well as helping to decrease the number of people affected by these events. 

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2. HEROES: Humanitarian Emergency Response based on UAV-enabled Integrated Sensing and Communication, Positioning, and Satisfaction Games

   https://doi.org/10.1145/3715334  

   Siraj, Md Sadman; Charatsaris, Panagiotis; Diamanti, Maria; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2025, ACM Journal on Autonomous Transportation Systems)

   In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This article presents a novel Positioning, Navigation, and Timing (PNT) framework, namedHEROES, which leverages the UAV and integrated sensing and communication technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an emergency control center. HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a reinforcement learning model based on the Optimistic Q-learning with Upper Confidence Bound algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including minimum efficient satisfaction equilibrium, ensuring that the UAVs meet their quality of service constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies. 

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3. Super-Node Approximation With Convex Hulls Relaxation for Distribution System Restoration Using ERRs

   https://doi.org/10.1109/ACCESS.2024.3415548  

   Sharma, Santosh; Li, Qifeng (January 2024, IEEE Access)

   Emergency response resources (ERRs) such as mobile energy resources (MERs) and repair crews (RCs) play a pivotal role in the efficient restoration of power distribution systems after disasters. This paper presents a computationally tractable approach to utilize ERRs and post-disaster available distributed energy resources (PDA-DERs) in the restoration of disaster-impacted distribution systems. The post-disaster restoration model is proposed to co-optimize the dispatch of pre-allocated ERRs and PDADERs to minimize the impact of high-impact low-frequency (HILF) events on customers, i.e., energy not served for the entire restoration window. Compared with existing restoration strategies using ERRs, the proposed approach is more tractable since, in the restoration model, a super-node approximation (SNA) of distribution networks and the convex hulls relaxation (CHR) of non-linear constraints are introduced to achieve the best trade-off between computational burden and accuracy. Tests of the proposed approach on IEEE test feeders demonstrated that a combination of SNA and CHR remarkably reduces the solution time of the post-disaster restoration model. 

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4. A Hierarchical Decision-making Process in Social Networks for Disaster Management

   Lee, Seunghan; Son, Young-Jun (May 2018, IISE Annual Conference and Expo 2018)

   The social media have been increasingly used for disaster management (DM) via providing real time data on a broad scale. For example, some smartphone applications (e.g. Disaster Alert and Federal Emergency Management Agency (FEMA) App) can be used to increase the efficiency of prepositioning supplies and to enhance the effectiveness of disaster relief efforts. To maximize utilities of these apps, it is imperative to have robust human behavior models in social networks with detailed expressions of individual decision-making processes and of the interactions among people. In this paper, we introduce a hierarchical human behavior model by associating extended Decision Field Theory (e-DFT) with the opinion formation and innovation diffusion models. Particularly, its expressiveness and validity are addressed in three ways. First, we estimate individual’s choice patterns in social networks by deriving people’s asymptotic choice probabilities within e-DFT. Second, by analyzing opinion formation models and innovation diffusion models in different types of social networks, the effects of neighbor’s opinions on people and their interactions are demonstrated. Finally, an agent-based simulation is used to trace agents’ dynamic behaviors in different scenarios. The simulated results reveal that the proposed models can be used to establish better disaster management strategies in natural disasters. 

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5. Sarcasm Detection in a Disaster Context

   Sosea, Tiberiu; Li, Junyi Jessy; Caragea, Cornelia (May 2024, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024))

   During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about the unfolding event or public policies and guidelines. This contempt is in some cases expressed as sarcasm or irony. Understanding this form of speech in a disaster-centric context is essential to improving natural language understanding of disaster-related tweets. In this paper, we introduce HurricaneSARC, a dataset of 15,000 tweets annotated for intended sarcasm, and provide a comprehensive investigation of sarcasm detection using pre-trained language models. Our best model is able to obtain as much as 0.70 F1 on our dataset. We also demonstrate that the performance on HurricaneSARC can be improved by leveraging intermediate task transfer learning 

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