arXiv:2607. 24856v1 Announce Type: cross Abstract: Social media imagery (SMI) provides timely and fine-grained ground perspectives that are valuable for situational awareness and emergency response.
Rapid extraction of structured information from social media is important for humanitarian response, yet existing disaster tweet resources mainly provide document-level category labels without span-le...
HUMAID-NER is the first named entity recognition dataset built on the HumAID benchmark, comprising 60,000 English disaster tweets with approximately 175,000 labeled entity spans across ten operationally motivated entity types. The dataset was created using a reproducible three‑stage hybrid pipeline that combines a spaCy transformer model, disaster‑domain EntityRuler patterns, and structured regular expressions with priority‑based overlap resolution. A joint multitask learning framework using a shared RoBERTa‑large encoder and homoscedastic uncertainty weighting achieves an NER span micro‑F1 of 0.841 and classification macro‑F1 of 0.761, and the authors provide a real‑time web dashboard, dataset, models, and pipeline code for reproducibility.
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:2609.00454v1 Announce Type: new Abstract: Pretrained transformer-based language models achieve strong performance across a wide range of NLP tasks but remain limited in encoding geo-locational...
IPGeoAI is a transformer-based deep learning model that transforms IP geolocation from a static lookup into a sequential modeling task. It captures hierarchical dependencies in IP subnet structures and resolves geographic ambiguity by fusing unstructured semantic context extracted from Autonomous System descriptions via a zero‑shot LLM feature extraction pipeline. Extensive offline evaluation on a proprietary dataset of 200,000 cities shows IPGeoAI outperforms a leading vendor, achieving a 6% improvement in city‑level accuracy and 100% traffic coverage, while online production tests demonstrate a statistically significant +0.35% improvement in first‑tier downstream use‑case metrics.
arXiv:2607. 22655v1 Announce Type: new Abstract: Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience.
The paper introduces BEACON, a multilingual agent system designed to deliver personalized wildfire evacuation guidance to users, including navigation routes, checklists, and a chatbot that adapts to the user’s language. It integrates real‑time fire perimeter, evacuation orders, shelter data, GPS, and NOAA weather to predict fire danger using an XGBoost model and triggers alerts with polygon‑avoidant routing when danger is likely. The interface automatically switches languages based on the user’s recent settings or chatbot interactions, ensuring inclusive communication for non‑English speakers.
arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.
arXiv:2608.20548v1 Announce Type: cross Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...
arXiv:2607. 23237v1 Announce Type: cross Abstract: Effective flood risk management relies on accurate forecasting, yet the "black box" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions.
arXiv:2601. 21149v3 Announce Type: replace-cross Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates.