A dynamic algorithm for wildfire mapping with NOAA/AVHRR data

2004 ◽  
Vol 13 (3) ◽  
pp. 275 ◽  
Author(s):  
R. Pu ◽  
P. Gong ◽  
Z. Li ◽  
J. Scarborough
Keyword(s):  
Developmental Stages ◽  
Riparian Forest ◽  
Fire Detection ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Fire Dynamics ◽  
California Department ◽  
Burn Scar ◽  
Dynamic Algorithm ◽  
Noaa Avhrr

A wildfire-mapping algorithm is proposed based on fire dynamics, called the dynamic algorithm. It is applied to daily NOAA/AVHRR/HRPT data for wildland areas (scrub, chaparral, grassland, marsh, riparian forest, woodland, rangeland and forests) in California for September and October 1999. Daily AVHRR images acquired from two successive days are compared for active fire detection and burn scar mapping. The algorithm consists of four stages: data preparation; hotspot detection; burn scar mapping; and final confirmation of potential burn scar pixels. Preliminary comparisons between the result mapped by the dynamic algorithm and the fire polygons collected by the California Department of Forestry and Fire Protection through ground survey indicate that the algorithm can track burn scars at different developmental stages at a daily level. The comparisons between wildfire mapping results produced by a modified version of an existing algorithm and the dynamic algorithm also indicate this point. This is the major contribution of this algorithm to wildfire detection methods. The dynamic algorithm requires highly precise registration between consecutive images.

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

Contextual algorithm adapted for NOAA-AVHRR fire detection in Indonesia

1999 ◽  
Vol 20 (17) ◽  
pp. 3415-3421 ◽  
Author(s):  
M. Nakayama ◽  
M. Maki ◽  
C. D. Elvidge ◽  
S. C. Liew
Keyword(s):  
Fire Detection ◽  
Noaa Avhrr

scholarly journals Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xi Cheng
Keyword(s):  
Search Algorithm ◽  
Fire Detection ◽  
Visual Task ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
Detection Model ◽  
Automatic Feature Extraction ◽  
Transfer Method ◽  
Research On Application

Most of the existing smoke detection methods are based on manual operation, which is difficult to meet the needs of fire monitoring. To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. The preselected area coordinates and the sample image of visual task are used as network learning. During the training process, we use the feature migration method to avoid the lack of smoke data or limited data sources. Finally, a target detection model is obtained, which is strongly related to a specified visual task, and it has well-trained weight parameters. Experimental results show that this method not only improves the detection accuracy but also effectively reduces the false alarm rate. It can not only meet the real time and accuracy of fire detection but also realize effective fire detection. Compared with similar fire detection algorithms, the improved algorithm proposed in this paper has better robustness to fire detection and has better performance in accuracy and speed.

scholarly journals Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2202 ◽  
Author(s):  
MinJi Park ◽  
Byoung Chul Ko
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Fire Detection ◽  
Classification Performance ◽  
Performance Comparison ◽  
Detection Methods ◽  
Night Time ◽  
Detection Systems ◽  
Ambient Lighting ◽  
Fast Classification

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

scholarly journals Wildfire ignition probability in Belgium

2020 ◽  
Vol 20 (2) ◽  
pp. 363-376 ◽  
Author(s):  
Arthur Depicker ◽  
Bernard De Baets ◽  
Jan Marcel Baetens
Keyword(s):  
Emergency Services ◽  
Action Plan ◽  
Fire Detection ◽  
Natura 2000 ◽  
Detection Methods ◽  
Fire Season ◽  
Ignition Probability ◽  
Wildfire Risk ◽  
Risk Framework ◽  
The Government

Abstract. In recent decades, large wildfires have inflicted considerable damage on valuable Natura 2000 regions in Belgium. Despite these events and the general perception that global change will exacerbate wildfire prevalence, this has not been studied yet in the Belgian context. Therefore, the national government initiated the national action plan on wildfires in order to evaluate the wildfire risk, on the one hand, and the materials, procedures, and training of fire services, on the other hand. This study focuses on the spatial distribution of the ignition probability, a component of the wildfire risk framework. In a first stage, we compile a historical wildfire database using (i) newspaper articles between 1994 and 2016 and (ii) a list of wildfire interventions between 2010 and 2013, provided by the government. In a second stage, we use a straightforward method relying on Bayes' rule and a limited number of covariates to calculate the ignition probability. It appears that most wildfire-prone areas in Belgium are located in heathland where military exercises are held. The provinces that have the largest relative areas with a high or very high wildfire risk are Limburg and Antwerp. Our study also revealed that most wildfire ignitions in Belgium are caused by humans (both arson and negligence) and that natural causes such as lightning are rather scarce. Wildfire prevention can be improved by (i) excluding military activity in fire-prone areas during the fire season, (ii) improving collaboration with foreign emergency services, (iii) concentrating the dedicated resources in the areas that display the highest ignition probabilities, (iv) improving fire detection methods, and (v) raising more awareness among the public.

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