scholarly journals Deep-Gap: A Deep Learning Framework for Forecasting Crowdsourcing Supply-Demand Gap Based on Imaging Time Series and Residual Learning

2019 ◽  
Author(s):  
Ahmed Ben Said ◽  
Abdelkarim Erradi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Framework ◽  
Residual Learning ◽  
Imaging Time

Privacy-preserving time series medical images analysis using a hybrid deep learning framework

2020 ◽  
Author(s):  
Zijie Yue ◽  
Shuai Ding ◽  
Lei Zhao ◽  
Youtao Zhang ◽  
Zehong Cao ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Medical Images ◽  
Privacy Preserving ◽  
Learning Framework

scholarly journals Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework

2021 ◽  
Vol 13 (9) ◽  
pp. 1738
Author(s):  
Huiyuan Fu ◽  
Yuchao Zheng ◽  
Yudong Ye ◽  
Xueshang Feng ◽  
Chaoxu Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Arrival Time ◽  
Optical Observations ◽  
Feature Maps ◽  
Feature Map ◽  
Learning Framework ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Better Than

Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites’ optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.

scholarly journals Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yifang Ban ◽  
Puzhao Zhang ◽  
Andrea Nascetti ◽  
Alexandre R. Bevington ◽  
Michael A. Wulder
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Real Time ◽  
Emergency Response ◽  
Critical Role ◽  
Synthetic Aperture ◽  
Learning Framework ◽  
Burnt Areas ◽  
Log Ratio ◽  
Northern Alberta

AbstractIn recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.

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