CrisiLangs is a multilingual extension of the CrisiText dataset, adding Italian, Bulgarian, German, and Greek translations of warning messages for 1,000 crisis events, resulting in 5,000 event‑message pairs across five languages. The resource was used to fine‑tune a multilingual model, which outperformed a zero‑shot baseline on ROUGE‑L, BLEU, and BERTScore in every language and configuration. Human preference studies showed that low decoding temperatures were favored across all languages, while the impact of emergency guidelines varied by language.
By Giacomo Gonella, Stefano Menini, Marco Guerini
arXiv:2608. 14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia.
By Anuridhi Gupta, Samara Mansoor, Hemant Purohit
Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios.
arXiv:2610.32842v1 Announce Type: new
Abstract: It is essential that we communicate with the entirety of an affected population during a crisis, including those who do not speak a region's dominant l...
By Jen Wilson, Vipasha Bansal, William D. Lewis
arXiv:2512.18225v2 Announce Type: replace
Abstract: This paper presents an applied AI pipeline for real-time geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-...
By Deepit Sapru
arXiv:2607. 24588v1 Announce Type: new Abstract: Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events.
By Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu
The paper introduces a simulation‑grounded vision‑language model (VLM) framework for wildfire monitoring that converts 2D wildfire simulations into labeled video episodes using a fixed Blender mapping to create low‑detail 3D proxies. These proxies, along with controllable video generation, provide a multimodal memory that a training‑free multi‑agent VLM system uses to retrieve reference episodes, reconcile visual and memory‑based predictions, and generate structured wildfire reports. The system achieves 77.3% accuracy on six simulator‑derived report fields, outperforming direct VLM querying and text‑only memory baselines.
By Duowen Chen, Yuchen Sun, Zhiqi Li, Yuxuan Liao, Sinan Wang, Bart van Bloemen Waanders, Bo Zhu
arXiv:2608. 20320v1 Announce Type: new Abstract: Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated separately.
By Narges Ahmadi (McGill University), Yubo Jiao (McGill University), J\^onatas Augusto Manzolli (McGill University), Jiangbo Yu (McGill University), Luis Miranda-Moreno (McGill University)
Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation.
The paper introduces SmartWeatherAgent, a three‑stage architecture that combines intent recognition, hazard prediction, and reasoning‑enhanced generation to improve tourism meteorological services. It fuses rule‑based methods with large language models and a LightGBM model enriched with highland‑specific features, achieving an F1‑Macro score of 0.605 and 1.60 ms latency on high‑wind, precipitation, and low‑temperature events. A 12‑round micro‑step prompt self‑optimization loop raises the composite warning quality score from 4.2 to 8.9, with notable gains in data source citation, physical mechanism explanation, and scientific rigor through explicit uncertainty statements.
By Shuai Yan, Yang Xu, Shan He
arXiv:2607. 17701v1 Announce Type: new Abstract: Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions.
By Guanzhen Li, Liangming Pan, Leye Wang
AI4Fire evaluates large language models on wildfire-related tasks, comparing bare (task input only) and grounded (task input plus a task‑specific addition) settings. The study tests six core models on five tasks in zero‑shot mode and expands to 29 additional models, revealing that grounding improves performance when the added information contains the answer, while simple rule‑based approaches often outperform the models. The paper also highlights risks in public releases, such as data leakage that can reveal sensitive information.
By Yue Zhao, Xiyang Hu, Zuobin Xiong, Zhangyu Wang, Ruolin Li