AI Research

UCLA researchers unveil AI-powered tool for near real-time, large-scale wildfire fuel mapping

Published

4 weeks ago

August 15, 2025

UCLA Samueli

Key takeaways:

In validation tests using data from two of California’s most intense recent wildfires — the Dixie and Caldor fires of 2021 — FuelVision’s predictions closely matched actual fuel maps.

FuelVision operates autonomously, utilizing commonly available data.

Because it draws data from global satellite inputs, FuelVision is readily adaptable to forested areas nationwide.

Researchers from the UCLA Samueli School of Engineering and their collaborators have developed FuelVision, a new system that could help enhance nationwide wildfire preparedness by combining satellite imagery with artificial intelligence to rapidly and accurately identify wildfire fuel sources.

In validation tests using data from two of California’s most intense recent wildfires — the Dixie and Caldor fires of 2021 — FuelVision’s predictions closely matched actual fuel maps, demonstrating the tool’s potential for real-world use. The system achieved 77% mapping accuracy in the tests. A study describing the new system was recently published in the International Journal of Applied Earth Observation and Geoinformation.

“We’ve built a tool that lets anyone — from local agencies to global researchers — generate wildfire fuel maps using satellite data,” said Riyaaz Shaik, lead author of the study and a research scientist at UCLA. “That helps make vital wildfire risk information accessible for faster, smarter response.”

Although some models have achieved higher accuracy on large scales, they are slower and rely on expert analysis. In contrast, FuelVision operates autonomously, utilizing commonly available data.

Because it draws data from global satellite inputs, FuelVision is readily adaptable to forested areas nationwide. The system does not require ground surveys to support fire-mitigation strategies or guide emergency responses.

Riyaaz Shaik/UCLA

Fuels map of the 2021 Caldor Fire, as predicted by FuelVision

To test and validate their model, the researchers trained the system using real data from the Forest Inventory and Analysis program of the U.S. Forest Service. The team also utilized generative adversarial networks, a type of machine learning that uses a generator to create data and a discriminator that evaluates the data’s accuracy, to produce reliable synthetic training data and help improve the system’s mapping accuracy.

“FuelVision can help anticipate where fires might spread and how to prepare,” said Ertugrul Taciroglu, study corresponding author and a professor of civil and environmental engineering at UCLA Samueli. “It’s versatile, easily adaptable and can help agencies globally with both organizing emergency response and developing long-term risk assessment and fire mitigation strategies.”

The researchers are making FuelVision accessible in two ways. They plan to release a Python-based interface that allows users with basic coding experience to generate their own fuel maps. They will also offer on-demand fuel-map production based on user needs.

The paper’s other authors are Mohamad Alipour of the University of Illinois Urbana-Champaign, a former postdoctoral researcher at UCLA; Eric Rowell, a specialist affiliated with the California Air Resources Board; Bharathan Balaji, a senior applied scientist at Amazon; and Adam Watts of the United States Forest Service Pacific Wildland Fire Sciences Lab.

The study was funded by the National Science Foundation, Edison International and the Science Hub for Humanity and Artificial Intelligence, which is a collaboration between UCLA Samueli and Amazon.

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AI Research

Artificial Intelligence Cheating | Nation

Published

53 minutes ago

September 12, 2025

Jae C. Hong - AP

Artificial Intelligence Cheating | Nation | hjnews.com

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Artificial Intelligence in Healthcare Market : A Study of

Published

1 hour ago

September 12, 2025

The Editors

Global Artificial Intelligence in Healthcare Market size was valued at USD 27.07 Bn in 2024 and is expected to reach USD 347.28 Bn by 2032, at a CAGR of 37.57%

Artificial Intelligence (AI) in healthcare is reshaping the industry by enabling faster diagnosis, personalized treatment, and enhanced operational efficiency. AI-driven tools such as predictive analytics, natural language processing, and medical imaging analysis are empowering physicians with deeper insights and decision support, reducing human error and improving patient outcomes. Moreover, AI is revolutionizing drug discovery, clinical trial optimization, and remote patient monitoring, making healthcare more proactive and accessible in both developed and emerging markets.

The adoption of AI in healthcare is also being accelerated by the rising demand for telemedicine, wearable health devices, and real-time data-driven solutions. From virtual health assistants to robotic surgery, AI is driving innovation across patient care and hospital management. However, challenges such as data privacy, ethical considerations, and regulatory frameworks remain crucial in ensuring responsible deployment. As AI continues to integrate with IoT, cloud, and big data platforms, it is set to create a connected healthcare ecosystem that prioritizes precision medicine and patient-centric solutions.

Get a sample of the report https://www.maximizemarketresearch.com/request-sample/21261/

Major companies profiled in the market report include

BP Target Neutral . JPMorgan Chase & Co. . Gold Standard Carbon Clear . South Pole Group . 3Degrees . Shell. EcoAct.

Research objectives:

The latest research report has been formulated using industry-verified data. It provides a detailed understanding of the leading manufacturers and suppliers engaged in this market, their pricing analysis, product offerings, gross revenue, sales network & distribution channels, profit margins, and financial standing. The report’s insightful data is intended to enlighten the readers interested in this business sector about the lucrative growth opportunities in the Artificial Intelligence in Healthcare market.

Get access to the full description of the report @ https://www.maximizemarketresearch.com/market-report/global-artificial-intelligence-ai-healthcare-market/21261/

It has segmented the global Artificial Intelligence in Healthcare market

by Offering

Hardware

Software

Services

by Technology

Machine Learning

Natural Language Processing

Context-Aware Computing

Computer Vision

Key Objectives of the Global Artificial Intelligence in Healthcare Market Report:

The report conducts a comparative assessment of the leading market players participating in the globalArtificial Intelligence in Healthcare

The report marks the notable developments that have recently taken place in the Artificial Intelligence in Healthcare industry

It details on the strategic initiatives undertaken by the market competitors for business expansion.

It closely examines the micro- and macro-economic growth indicators, as well as the essential elements of theArtificial Intelligence in Healthcaremarket value chain.

The repot further jots down the major growth prospects for the emerging market players in the leading regions of the market

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A Unified Model for Robot Interaction, Reasoning and Planning

Published

3 hours ago

September 12, 2025

Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li

[Submitted on 1 Sep 2025 (v1), last revised 11 Sep 2025 (this version, v2)]

View a PDF of the paper titled Robix: A Unified Model for Robot Interaction, Reasoning and Planning, by Huang Fang and 8 other authors

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Abstract:We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments demonstrate that Robix outperforms both open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.