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.
arXiv:2609.35821v2 Announce Type: replace Abstract: Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian response, but plausible LLM-gen...
arXiv:2610.15403v1 Announce Type: new Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geographi...
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...
The paper introduces GRDisaster, a multi-task geospatial reasoning framework that leverages vision‑language models to interpret, geolocalize, and assess damage in crowdsourced disaster imagery. It builds on a new benchmark dataset of 26,340 images from PhotoMappers, linking volunteer geographic information, street‑view imagery, and remote sensing data across multiple disaster events from 2018 to 2024. GRDisaster combines deterministic and probabilistic cross‑view geolocalization with multi‑view fusion, and introduces spatial reasoning indicators to validate cross‑view matches and quantify disaster severity using expert‑verified annotations.
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.
arXiv:2610.14624v1 Announce Type: cross Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive se...
arXiv:2610.24306v2 Announce Type: replace-cross Abstract: Location encoders learn compact representations of latitude--longitude coordinates for downstream geospatial prediction tasks. Existing appro...