Search Results (40)

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

The growing complexity and risk profile of fire and emergency incidents necessitate advanced training methodologies that go beyond traditional approaches. Live-fire drills and classroom-based instruction, while foundational, often fall short in providing safe, repeatable, and scalable training environments that accurately reflect the dynamic nature of real-world emergencies. Recent advancements in immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and simulation-based systems, offer promising alternatives to address these challenges. This review provides a comprehensive overview of the integration of VR, AR, MR, XR, and simulation technologies into firefighter and incident commander training. It examines current practices across fire services and emergency response agencies, highlighting the capabilities of immersive and interactive platforms to enhance operational readiness, decision-making, situational awareness, and team coordination. This paper analyzes the benefits of these technologies, such as increased safety, cost-efficiency, data-driven performance assessment, and personalized learning pathways, while also identifying persistent challenges, including technological limitations, realism gaps, and cultural barriers to adoption. Emerging trends, such as AI-enhanced scenario generation, biometric feedback integration, and cloud-based collaborative environments, are discussed as future directions that may further revolutionize fire service education. This review aims to support researchers, training developers, and emergency service stakeholders in understanding the evolving landscape of digital training solutions, with the goal of fostering more resilient, adaptive, and effective emergency response systems. Full article

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data. Full article

In many European cities, old urban centres, particularly historical centres, reveal significant limitations to the intervention of civil protection agents—namely, firefighters—in terms of their mobility and action in emergencies, thus conditioning their action. This article analyses the case of a Portuguese city (the city of Ílhavo), proposing possible solutions applicable to territories with these specificities. This study was developed with the support of a literature review and field work that accompanied the actions of the local fire department. The proposals include new technical solutions (such as underground dry pipeworks), measures regarding traffic restrictions, the adequacy of signage, and recommendations for training the population living in these areas to intervene in fire situations. This study was developed through monitoring several interventions by the local fire department in real emergencies, allowing for the identification of some existing limitations in its activity resulting from the specific characteristics of the old urban centre. Measures already adopted in other Portuguese cities to improve mobility and reduce the negative impact on firefighters’ work in historical centres are also mentioned. It is also recommended that all civil protection agents be closely involved in planning and designing these urban rehabilitation interventions when carrying them out in these areas. Full article

In response to the escalating complexity and frequency of wildland fires, this study investigates the feasibility of using wearable devices for real-time monitoring of cardiac, respiratory, physical, and environmental parameters during live wildfire suppression tasks. Data were collected from twelve male firefighters (FFs) from the Italian National Fire Corp during a simulated protocol, including rest, running, and active fire suppression phases. Physiological and physical metrics such as heart rate (HR), heart rate variability (HRV), respiratory frequency (f_R) and physical activity levels were extracted using chest straps. The protocol designed to mimic real-world firefighting scenarios revealed significant cardiovascular and respiratory strain, with HR often exceeding 85% of age-predicted maxima and sustained elevations in high-stress roles. Recovery phases highlighted variability in physiological responses, with reduced HRV indicating heightened autonomic stress. Additionally, physical activity analysis showed task-dependent intensity variations, with debris management roles exhibiting consistently high exertion levels. These findings demonstrate the relevance of wearable technology for real-time monitoring, providing an accurate analysis of key metrics to offer a comprehensive overview of work-rest cycles, informing role-specific training and operational strategies. Full article

Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models’ (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests’ (RF) 0.5662 and GBM’s 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau. Full article

Firefighters are occupationally exposed to many chemicals, including polycyclic aromatic hydrocarbons (PAHs), which are formed by the incomplete combustion of organic matter during fire response and training activities. However, due to the harsh environments in which firefighters work, as well as consideration for time and physical safety while wearing bulky equipment, traditional active sampling methods may not be feasible to measure PAH exposures. Silicone passive samplers offer an alternative approach to assess exposure during fire responses and live fire training due to their heat resistance and ease of deployment in remote or time-limited environments. In this study, the primary objective was to investigate and determine the statistical strength of the relationship between active air sampling methods and passive silicone samplers for PAHs. In this study, silicone wristbands were paired with active sampling devices in a series of burn experiments to compare PAH measurements. Silicone-based measurements correlated strongly with active air samples for the dominant PAHs found, naphthalene and phenanthrene; however, detection was limited in the wristbands when air concentrations were low in active samples. In situations where PAH levels are expected to be high and the potential for contaminant loss via off-gassing is low, silicone samplers may be a useful tool for industrial hygienists to measure PAHs in fire and other emergency responses in extreme environments. Full article

Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article

Invasive red imported fire ants (Solenopsis invicta) create a serious threat to public safety, agriculture, biodiversity, and the local economy, necessitating early detection and surveillance, which are currently time-consuming and dependent on the inspector’s expertise. This study marks an initial investigation into the potential of honeybees (Apis mellifera) to detect and discriminate the odor of S. invicta through the olfactory conditioning of proboscis extension responses. Deceased S. invicta were used as conditioned stimuli to ensure relevance to non-infested areas. The results showed that the bees rapidly learned to respond to deceased ant odors, with response levels significantly increasing at higher odor intensities. Bees exhibited generalization across the odors of 25 minor workers, 21 median workers, 1 major worker, and 1 female alate. When conditioned with deceased ant odors, bees effectively recognized live ants, particularly when trained on a single minor worker. Discrimination abilities varied by species and were higher when S. invicta was paired with Polyrhachis dives and Nylanderia yaeyamensis, and lower with S. geminata, Pheidole rabo, and Pheidole fervens. Notably, discrimination improved significantly with the application of latent inhibition. These findings suggest that trained honeybees have the potential to detect S. invicta. Further refinement of this approach could enhance its effectiveness for detection and surveillance. Full article

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning. Full article

Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied. Full article

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires. Full article

Crop residue burning (CRB) is a major source of air pollution in many parts of the world, especially Asia. Policymakers, practitioners, and researchers have invested in measuring the extent and impacts of burning and developing interventions to reduce its occurrence. However, any attempt to measure burning, in terms of its extent, impact, or the effectiveness of interventions to reduce it, requires data on where burning occurs. These data are challenging to collect in the field, both in terms of cost and feasibility, because crop-residue fires are short-lived, each covers only a small area, and evidence of burning disappears once fields are tilled. Remote sensing offers a way to observe fields without the complications of on-the-ground monitoring. However, the same features that make CRB hard to observe on the ground also make remote-sensing-based measurements prone to inaccuracies. The extent of crop burning is generally underestimated due to missing observations, while individual plots are often falsely identified as burned due to the local dominance of the practice, a lack of training data on tilled vs. burned plots, and a weak signal-to-noise ratio that makes it difficult to distinguish between the two states. Here, we summarize the current literature on the measurement of CRB and flag five common pitfalls that hinder analyses of CRB with remotely sensed data: inadequate spatial resolution, inadequate temporal resolution, ill-fitted signals, improper comparison groups, and inadequate accuracy assessment. We take advantage of data from ground-based monitoring of CRB in Punjab, India, to calibrate and validate analyses with PlanetScope and Sentinel-2 imagery and illuminate each of these pitfalls. We provide tools to assist others in planning and conducting remote sensing analyses of CRB and stress the need for rigorous validation. Full article

Live Fuel Moisture Content (LFMC) describes the amount of water present in any type of vegetation and helps quantify the amount of fuel available in a wildfire. In this paper, a multivariate linear regression model was built to estimate the LFMC of the weighted average of all shrub-type species present, using the fraction of canopy cover (FCC) of each forest species as weights. Sample training was conducted with field data obtained during the fire season of the years 2019, 2020 and 2021 in 15 plots of a Mediterranean area where vegetation composed of the shrub-type species dominates. Different spectral indices extracted from Sentinel-2 together with the mean surface temperature, the accumulated precipitation and the seasonal parameters were considered as predictors. The results were compared with the extrapolation of another model trained with field data collected in the year 2019. Full article

This study aimed to investigate the disaster or emergency coping skills of people with intellectual disabilities and the factors that affect these skills. The panel survey on the lives of people with disabilities from the 3rd dataset (2020) of the Korea Development Institute for the Disabled was used for this analysis. Response data from 275 people with intellectual disabilities aged 10 years or older were analyzed. Differences between disaster or emergency coping skill levels and sub-questions of skills, according to the general characteristics of people with intellectual disabilities, were identified, as well as factors affecting the level of disaster or emergency coping skills. The results show that the coping skills level was low; among the sub-questions, the use of fire extinguishers and awareness of the location of fire extinguishers or emergency bells in the event of a disaster or emergency were also low. Factors affecting the level of coping skills were found to be the level of education and experience in comprehensive disaster coping training. The results of this study suggest that training and education on disaster or emergency coping skills for people with intellectual disabilities are necessary and that programs should be developed for this purpose. Full article