In recent years, social media has emerged as a primary channel for users to promptly share feedback and issues during disasters and emergencies, playing a key role in crisis management. While significant progress has been made in collecting and analyzing social media content, there remains a pressing need to enhance the automation, aggregation, and customization of this data to deliver actionable insights tailored to diverse stakeholders, including the press, police, EMS, and firefighters. This effort is essential for improving the coordination of activities such as relief efforts, resource distribution, and media communication. This paper presents a methodology that leverages the capabilities of LLMs to enhance disaster response and management. Our approach combines classification techniques with generative AI to bridge the gap between raw user feedback and stakeholder-specific reports. Social media posts shared during catastrophic events are analyzed with a focus on user-reported issues, service interruptions, and encountered challenges. We employ full-spectrum LLMs, using analytical models like BERT for precise, multi-dimensional classification of content type, sentiment, emotion, geolocation, and topic. Generative models such as ChatGPT are then used to produce human-readable, informative reports tailored to distinct audiences, synthesizing insights derived from detailed classifications. We compare standard approaches, which analyze posts directly using prompts in ChatGPT, to our advanced method, which incorporates multi-dimensional classification, sub-event selection, and tailored report generation. Our methodology demonstrates superior performance in both quantitative metrics, such as text coherence scores and latent representations, and qualitative assessments by automated tools and field experts, delivering precise insights for diverse disaster response stakeholders.

Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data from the Environmental Protection Agency and historical wildfire data from 2012 to 2018 have been compiled to establish a comprehensive wildfire database, the largest of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural network was designed with a special architecture for extracting key spatial and temporal features from contiguous environmental parameters indicative of impending wildfires. Environmental and meteorological factors were incorporated into the database and classified as leading indicators and trailing indicators, correlated to risks of wildfire conception and propagation respectively. Additionally, geological data was used to provide better wildfire risk assessment. This novel spatio-temporal neural network achieved >97% accuracy vs. around 76% using traditional convolutional neural networks, successfully predicting 2018's five most devastating wildfires 5-14 days in advance. Finally, a personalized early warning system, tailored to individuals with sensory disabilities or respiratory exacerbation conditions, was proposed. This technique would enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives and reducing economic damages.

People around the globe have suffered the nerve-wracking anxiety of waiting weeks or months to find out if their homes have been damaged by wildfires that scorch with increased intensity. Now, once the smoke has cleared for aerial photography, researchers have found a way to identify building damage within minutes. Through a system they call DamageMap, a team at Stanford University and the California Polytechnic State University (Cal Poly) has brought an artificial intelligence approach to building assessment: Instead of comparing before-and-after photos, they've trained a program using machine learning to rely solely on post-fire images. The findings appear in the International Journal of Disaster Risk Reduction. "We wanted to automate the process and make it much faster for first responders or even for citizens that might want to know what happened to their house after a wildfire," said lead study author Marios Galanis, a graduate student in the Civil and Environmental Engineering Department at Stanford's School of Engineering.

Lightning was a factor in many of these fires. But past blazes, including the 2018 Camp Fire that destroyed the town of Paradise, Calif., were started by faulty transmission equipment. In that case, a worn piece of metal that holds power lines, known as a C-hook, broke and dropped a high-voltage electric line that ignited that fire. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. In June, PG&E Corp., parent company of Pacific Gas and Electric Co., pleaded guilty to 84 counts of involuntary manslaughter for its role in sparking that fire.

Based on the last two wildfire seasons, including 2018 when an entire California town was destroyed, utilities blamed for recent wildfires need all the help they can get maintaining aging grids. AI technologies may provide new monitoring tools. Paradise, Calif., population of about 27,000, was destroyed by the Camp Fire. The 2018 inferno claimed at least 84 victims. In June, Pacific Gas & Electric (PG&E) was ordered to pay a $3.5 million fine for causing the Camp Fire.

Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc. An ability to evaluate evacuation plans in advance for these rare events, including identifying traffic flow bottlenecks, improving traffic management policies, and understanding the robustness of the traffic management policy are critical for emergency management. Given the rareness of such events and the corresponding lack of real data, traffic simulation provides a flexible and versatile approach for such scenarios, and furthermore allows dynamic interaction with the simulated evacuation. In this paper, we build a traffic simulation pipeline to explore the above problems, covering many aspects of evacuation, including map creation, demand generation, vehicle behavior, bottleneck identification, traffic management policy improvement, and results analysis. We apply the pipeline to two case studies in California. The first is Paradise, which was destroyed by a large wildfire in 2018 and experienced catastrophic traffic jams during the evacuation. The second is Mill Valley, which has high risk of wildfire and potential traffic issues since the city is situated in a narrow valley.

AR and thermal imaging in the Qwake C-Thru mask could help firefighters better navigate burning buildings. With smoke, flames and a claustrophobic mask on, running into a burning building is a leap of faith. Firefighters are taught never to leave the wall, because they could become disoriented, run out of air and die. "The way we used to look for people was almost as if you were blind," said Harold Schapelhouman, fire chief of the Menlo Park Fire Protection District. That could change with technology like Qwake's C-Thru.