Results 31 to 40 of about 497,808 (317)
A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion [PDF]
Remote Sensing, 2021 Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions.
Zijun Yang, Chunyuan Diao, Bo Li
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Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
Applied Sciences, 2022 Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on.
Zichao He, Chunna Zhao, Yaqun Huang
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Correlation Net: Spatiotemporal multimodal deep learning for action recognition [PDF]
Signal Processing: Image Communication, 2020 This paper describes a network that captures multimodal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over a multimodal convolutional neural network (CNN). Spatial and temporal streams are required for action recognition by a deep CNN, but overfitting reduction and fusing these two streams ...
Novanto Yudistira +2 more
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Spatiotemporal Costmap Inference for MPC Via Deep Inverse Reinforcement Learning [PDF]
IEEE Robotics and Automation Letters, 2022 It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from human demonstrations.
Keuntaek Lee +3 more
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Geocarto International, 2023 Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL) model usefulness to forecast changes. To manage geospatial data imbalance, there is a lack of specialized cost-sensitive learning strategies available.
Alysha van Duynhoven +1 more
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Spatiotemporal Analysis by Deep Learning of Gait Signatures From Floor Sensors [PDF]
IEEE Sensors Journal, 2021 The recognition of gait pattern variation is of high importance to various industrial and commercial applications, including security, sport, virtual reality, gaming, robotics, medical rehabilitation, mental illness diagnosis, space exploration, and others.
Abdullah S. Alharthi +2 more
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A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services [PDF]
Mobile Networks and Applications, 2018 This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions.
Ahmed Ben Said +3 more
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Model for Spatiotemporal Crime Prediction with Improved Deep Learning
Computing and Informatics, 2023 Crime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this ...
Angbera, Ature, Chan, Huah Yong
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Geometric deep learning reveals the spatiotemporal features of microscopic motion
Nature Machine Intelligence, 2023 AbstractThe characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Owing to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles and individual molecules at multiple spatiotemporal scales in ...
Jesús Pineda +6 more
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Sensors, 2022 Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the ...
Sungkyu Kim, Tae-Seong Kim, Won Hee Lee
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