arXiv AI
Distilling Vision-Language Models for On-Device Fire Understanding
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arXiv AI
Sep 10
SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs
SAFIRE is a large-scale benchmark for fire and smoke understanding in multimodal large language models (MLLMs), featuring 83,000 captioned images across 20 scenarios and 193,000 multiple-choice VQA questions derived from a 9.7K-image subset. The benchmark evaluates 10 dimensions of performance, from basic perception to higher-order reasoning, and employs a GPT‑5.4-assisted verification pipeline to ensure annotation quality. Experiments on ten open-source MLLMs (8B–38B) reveal an average accuracy of 61.9%, highlighting significant gaps in safety-critical reasoning, while fine-tuning vision encoders on just 7% of SAFIRE data boosts fire-scene classification accuracy from 20.1% to 64.5%. All resources are publicly available at https://risys-lab.github.io/SAFIRE/.
arXiv Computer Vision
Oct 5
A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting
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.
arXiv Computer Vision
Sep 17
WISE: A Lightweight, Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization
arXiv:2609.18227v1 Announce Type: new Abstract: Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems m...
Hugging Face Trending Papers
Sep 16
WISE: A Lightweight, Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization
Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems must operate under strict memory and latency const...
arXiv Machine Learning
Oct 9
AI4Fire: Evaluating Large Language Models on Wildfire Tasks
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.
arXiv Computer Vision
Sep 21
DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response
DisasterInsight is a building‑centric benchmark designed to evaluate vision‑language models (VLMs) for disaster response. Built on the xBD satellite dataset, it adds OpenStreetMap‑derived functional labels to 134,108 building instances and offers 15 task types, including instance assessment, scene counting, multi‑instance reasoning, and structured report generation. Experiments show that VLMs excel at visible damage detection but struggle with building function, multi‑instance reasoning, counting, and grounded reporting, and instruction tuning only partially mitigates these gaps.