In this paper, we propose a novel benchmark, named VastTrack, aiming to facilitate the development of general visual tracking via encompassing abundant classes and videos. VastTrack consists of a few attractive properties: (1) Vast Object Category. In particular, it covers targets from 2,115 categories, significantly surpassing object classes of existing popular benchmarks (e.g., GOT-10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking.

Fire and smoke phenomena pose a significant threat to the natural environment, ecosystems, and global economy, as well as human lives and wildlife. In this particular circumstance, there is a demand for more sophisticated and advanced technologies to implement an effective strategy for early detection, real-time monitoring, and minimizing the overall impacts of fires on ecological balance and public safety. Recently, the rapid advancement of Artificial Intelligence (AI) and Computer Vision (CV) frameworks has substantially revolutionized the momentum for developing efficient fire management systems. However, these systems extensively rely on the availability of adequate and high-quality fire and smoke data to create proficient Machine Learning (ML) methods for various tasks, such as detection and monitoring. Although fire and smoke datasets play a critical role in training, evaluating, and testing advanced Deep Learning (DL) models, a comprehensive review of the existing datasets is still unexplored. For this purpose, we provide an in-depth review to systematically analyze and evaluate fire and smoke datasets collected over the past 20 years. We investigate the characteristics of each dataset, including type, size, format, collection methods, and geographical diversities. We also review and highlight the unique features of each dataset, such as imaging modalities (RGB, thermal, infrared) and their applicability for different fire management tasks (classification, segmentation, detection). Furthermore, we summarize the strengths and weaknesses of each dataset and discuss their potential for advancing research and technology in fire management. Ultimately, we conduct extensive experimental analyses across different datasets using several state-of-the-art algorithms, such as ResNet-50, DeepLab-V3, and YoloV8.

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.

Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation decision prediction, a Large Language Model (LLM)-based framework that integrates behavioral theories and models to streamline the Chain-of-Thought (CoT) reasoning and subsequently integrate with memory-based Reinforcement Learning (RL) module to provide accurate evacuation decision prediction and understanding. Our proposed method addresses the limitations of using existing LLMs for evacuation behavioral predictions, such as limited survey data, mismatching with behavioral theory, conflicting individual preferences, implicit and complex mental states, and intractable mental state-behavior mapping. Experiments on three post-wildfire survey datasets show an average of 20.47% performance improvement over traditional theory-informed behavioral models, with strong cross-event generalizability. Our complete code is publicly available at https://github.com/SusuXu-s-Lab/FLARE

Nowadays, traffic management in urban areas is one of the major economic problems. In particular, when faced with emergency situations like firefighting, timely and efficient traffic dispatching is crucial. Intelligent coordination between multiple departments is essential to realize efficient emergency rescue. In this demo, we present a framework that integrates techniques for collaborative learning methods into the well-known Unity Engine simulator, and thus these techniques can be evaluated in realistic settings. In particular, the framework allows flexible settings such as the number and type of collaborative agents, learning strategies, reward functions, and constraint conditions in practice. The framework is evaluated for an emergency rescue scenario, which could be used as a simulation tool for urban emergency departments.

Fire safety is a critical area of research in civil and mechanical engineering, particularly in ensuring the structural stability of buildings during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the location where a fire would cause the greatest impact on structural stability. Accurate prediction of the MFSP is vital for streamlining structural assessments and optimizing the design process. This paper presents a novel framework for MFSP prediction using a neural network-based approach that integrates fire dynamics and finite element analysis through a differentiable agent model. The framework focuses on predicting the Maximum Interstory Drift Ratio (MIDR), a key indicator of structural performance under fire conditions. By leveraging the differentiable agent model, we efficiently generate labeled data for MFSP and directly train a predictor for this critical metric. To achieve this, we generated extensive simulation data encompassing structural and fire scenarios and employed graph neural networks to represent the building structures. Transfer learning was applied to optimize the training process, and an edge update mechanism was introduced to dynamically adjust edge attributes, reflecting property changes under fire conditions. The proposed model was rigorously evaluated on simulation data, demonstrating strong performance in accurately predicting both MIDR and MFSP, thus advancing fire safety analysis for building structures.

As Los Angeles firefighters battle remaining hot spots more than a week into deadly blazes, scientists and engineers hope the growing availability of satellite data will help in the future. Tech-focused groups are launching new orbiters as space launches get cheaper, while machine-learning techniques will sift the torrent of information, fitting it into a wider picture of fire risk in a changing environment. Satellites "can detect from space areas that are dry and prone to wildfire outbreaks ... actively flaming and smouldering fires, as well as burnt areas and smoke and trace gas emissions. We can learn from all these types of elements," said Clement Albergel, head of actionable climate information at the European Space Agency.

The FBI recently confirmed a Canadian plane offering assistance during the California wildfires was damaged in a collision with a privately-owned drone. Police arrested three people following two drone incidents as authorities report numerous encounters with aerial operations, potentially hampering lifesaving measures as wildfires rage throughout Southern California. As of Monday afternoon, charges had not been released. Two arrests stem from one drone incident, according to Los Angeles County Sheriff Robert Luna. "If you do not have business in the evacuation areas, do not go there," Luna said in a press conference on Monday.

California Fire Battalion chief David Acuna joins'Fox & Friends Weekend' to provide an update on the ongoing Los Angeles fires. Private drones being flown near the wildfires consuming Los Angeles County continued to interfere with aerial firefighting efforts Saturday evening, according to officials, as the death toll from the flames rises. Officials have detected 48 privately owned drones flying over the fires since the infernos erupted Tuesday, Los Angeles County Deputy Fire Chief Robert Harris said during a briefing Saturday evening. "When those privately owned drones are detected, we have to pause firefighting activities, so we ask you to please assist us by not operating drones in the area," Harris said, adding that the drones' owners are being sought by police and will face potential prosecution. Authorities urge civilians not to fly drones near wildfires because they can get in the way of low-flying firefighting aircraft and delay emergency responders.

Multiple unauthorized drones flew above the Palisades fire Friday afternoon, forcing firefighting aircraft to leave the area for safety and angering those working on the front lines, authorities said. These sightings came just a day after a drone collided with a Super Scooper fixed-wing aircraft, grounding the plane for several days of repairs and reducing the number of aircraft available to fight the fire. "This is not just harmless fun. This is incredibly dangerous," said Chris Thomas, public information officer for the Palisades fire. "Seriously, what if that plane had gone down? It could have taken out a row of homes. It could have taken out a school."