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Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response

Minsoo Park, Dai Quoc Tran, Seungsoo Lee, Seunghee Park
2021 Remote Sensing  
This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work.  ...  Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13193985 fatcat:2hlmf5fpr5hlvjo3t6of4ydkjy

Optimized Deployment of Unmanned Aerial Vehicles for Wildfire Detection and Monitoring [article]

Tai Yang, Shumeng Zhang, Yong Wang, Jialei Liu
2021 arXiv   pre-print
Though a plethora of machine learning algorithms have been developed to detect wildfires using aerial images and videos captured by drones, there is a lack of methods corresponding to drone deployment.  ...  The lack of accurate frontline information in real-time can pose great risks to firefighters.  ...  A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. In Signal Processing (Vol. 190).  ... 
arXiv:2112.03010v1 fatcat:iuur5xy2onczlmouqbdlrtx5ti

Wildfire Detection from Multi-sensor Satellite Imagery Using Deep Semantic Segmentation

Dmitry Rashkovetsky, Florian Mauracher, Martin Langer, Michael Schmitt
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In contrast, more recently, deep learning has found its way into the application, having the advantage of being able to detect patterns in complex data by learning from examples automatically.  ...  Deriving the extent of areas affected by wildfires is critical to fire management, protection of the population, damage assessment, and better understanding of the consequences of fires.  ...  ACKNOWLEDGMENT This work was financially supported by the Munich University of Applied Sciences and the German Research Foundation (DFG) through the "Open Access Publishing" program.  ... 
doi:10.1109/jstars.2021.3093625 fatcat:2wieghpwtrbghleq4vgp55n3ta

Wildfire early warning system based on wireless sensors and unmanned aerial vehicle

Songsheng Li
2018 Journal of Unmanned Vehicle Systems  
The application 13 of this system in the environment will enhance the ability of wildfire prediction for the 14 community. 15  ...  Various wildfire monitoring systems are deployed in different countries, 4 most depend on photos or videos to identify features of wildfire after the first outbreak, 5 while the delay of confirmation varies  ...  Acknowledgements 478 Christopher Chiu, Faculty of Engineering, University of Technology, Sydney, christopher.chiu@uts.edu.au. 479  ... 
doi:10.1139/juvs-2018-0022 fatcat:e4ywlignnje2hejsfm2y6o7cg4

Wildfire Smoke Particulate Matter Concentration Measurements Using Radio Links From Cellular Communication Networks

Adrien Guyot, Jayaram Pudashine, Remko Uijlenhoet, Alain Protat, Valentijn R. N. Pauwels, Valentin Louf, Jeffrey P. Walker
2021 AGU Advances  
Plain Language Summary Unprecedented mega wildfires in southern and eastern Australia generated considerable amounts of smoke and subsequent hazardous health conditions in Australian capital cities.  ...  Dry air containing large amounts of smoke sitting above the ground acted as a lid, reducing dispersion, trapping and maintaining high ground-level concentrations of smoke.  ...  Long-Short Term Memory deep learning algorithms, in order to analyze and retrieve desired proxies, including PM concentrations or atmospheric gases such as CO, NO x , or O 3 .  ... 
doi:10.1029/2020av000258 fatcat:pn5uzwrxdrdabcwsslunkx4xxq

Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment

Yufei Zou, Susan M. O'Neill, Narasimhan K. Larkin, Ernesto C. Alvarado, Robert Solomon, Clifford Mass, Yang Liu, M. Talat Odman, Huizhong Shen
2019 International Journal of Environmental Research and Public Health  
of smoke hazards in fire-prone regions.  ...  Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW).  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/ijerph16122137 pmid:31212933 pmcid:PMC6617359 fatcat:hahfhth7qfgnhe6vtjs4yxtyqq

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Nikos Kalatzis, George Routis, Yiorgos Marinellis, Marios Avgeris, Ioanna Roussaki, Symeon Papavassiliou, Miltiades Anagnostou
2019 Sensors  
These specifications have been utilised for the implementation of the IoT2Edge interoperability enabling mechanism which is evaluated within the context of a catastrophic wildfire incident that took place  ...  in Greece on July 2018.  ...  This service, which was initially developed for the purposes of the work presented in [69] , leverages Google's Tensorflow deep learning framework.  ... 
doi:10.3390/s19030528 fatcat:cqke6qx6zjf4jnjijjcdznyz7y

Table of Contents

2021 IEEE Transactions on Power Systems  
Liu 3176 Block-Sparse Bayesian Learning Method for Fault Location in Active Distribution Networks With Limited Synchronized Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Dong 3453 Spatial Distribution of Grid Inertia and Dynamic Flexibility: Approximations and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tpwrs.2021.3084726 fatcat:hfbyo6bahjgbzezir7kzrogf3y

Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow [article]

Thomas Vandal, Ramakrishna Nemani
2020 arXiv   pre-print
Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the trade-offs to spatial, spectral  ...  In this work, we apply a task specific optical flow approach to temporal up-sampling using deep convolutional neural networks.  ...  of deep learning in the area.  ... 
arXiv:1907.12013v3 fatcat:a3rmiqlzvvaw7j6hoesekhmn24

2021 Index IEEE Transactions on Power Systems Vol. 36

2021 IEEE Transactions on Power Systems  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  R, R.M., +, TPWRS Sept. 2021 4052-4065 Deep learning A Deep Neural Network Approach for Online Topology Identification in State Estimation.  ... 
doi:10.1109/tpwrs.2021.3125235 fatcat:n3ecyy2flnapzjz7clyrp7sx4a

Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework

Mohsen Naderpour, Hossein Mojaddadi Rizeei, Fahimeh Ramezani
2021 Remote Sensing  
In this study, we propose a spatial framework to quantify the forest fire risk in the Northern Beaches area of Sydney.  ...  Optimized deep neural networks were developed to maximize the capability of the multilayer perceptron for forest fire susceptibility assessment.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13132513 fatcat:j7erdz4avjfh5brweyvba22xoe

Table of Contents

2022 IEEE transactions on industry applications  
Dehghanian Metal Industry Committee Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning . . . . . . . . . . . . . . . .  ...  Garcia Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tia.2022.3171990 fatcat:3jnopwxrn5gx3kwxpy2xsllz5e

IEEE Access Special Section Editorial: Emerging Trends of Energy and Spectrum Harvesting Technologies

Guangjie Han, Deyu Zhang, Ning Zhang, Song Guo, Geyong Min, Shengrong Bu, Kanke Gao
2021 IEEE Access  
His current H-index is 46 and i10-index is 172 in Google Citation (Google Scholar). In total, his articles have been cited more than 8800 times. He is a fellow of the U.K.  ...  His current research interests include the Internet of Things, industrial internet, machine learning and artificial intelligence, mobile computing, and security and privacy.  ...  Theoretical analysis in a more general case shows that the proposed algorithm can improve the convergence rate of distributed time synchronization when selecting the appropriate parameter, and the closed-form  ... 
doi:10.1109/access.2021.3105788 fatcat:un6us5dta5dnpbsbenh7ztttti

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Thomas J. Vandal, Ramakrishna R. Nemani
2021 IEEE Transactions on Neural Networks and Learning Systems  
In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery.  ...  Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral  ...  of deep learning in the area.  ... 
doi:10.1109/tnnls.2021.3101742 pmid:34375289 fatcat:thgcnnhfkvbplhgahx7a2nsesq

Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters [article]

Jing Pan, Wendao Liu, Jing Zhou
2020 arXiv   pre-print
The freedom of fast iterations of distributed deep learning tasks is crucial for smaller companies to gain competitive advantages and market shares from big tech giants.  ...  We also implemented the Rectified Adam optimizer for the first time in HorovodRunner.  ...  In fact, given the potential of unsupervised learning, deep reinforcement learning on graphs might have the most use cases in the industry.  ... 
arXiv:2005.05510v1 fatcat:7ngmxzzwqre2bgn355ix2ffq3y
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