Results 11 to 20 of about 497,808 (317)
Improving Spatiotemporal Self-supervision by Deep Reinforcement Learning [PDF]
, 2018 Accepted for publication at ECCV ...
Büchler, Uta +2 more
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Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning [PDF]
Journal of Sensors, 2020 High spatial and temporal resolution remote sensing data play an important role in monitoring the rapid change of the earth surface. However, there is an irreconcilable contradiction between the spatial and temporal resolutions of the remote sensing image acquired from a same sensor.
Xiaofei Wang, Xiaoyi Wang
openaire +2 more sources
A parallel spatiotemporal deep learning network for highway traffic flow forecasting [PDF]
International Journal of Distributed Sensor Networks, 2019 Spatiotemporal features have a significant influence on traffic flow prediction. Due to the potentially internal relationship of adjacent roads, spatial information can, to some extent, affect traffic flow forecasting.
Dongxiao Han, Juan Chen, Jian Sun
doaj +2 more sources
Spatiotemporal Deep Learning Model for Prediction of Taif Rose Phenotyping [PDF]
Agronomy, 2022 Despite being an important economic component of Taif region and the Kingdom of Saudi Arabia (KSA) as a whole, Taif rose experiences challenges because of uncontrolled conditions. In this study, we developed a phenotyping prediction model using deep learning (DL) that used simple and accurate methods to obtain and analyze data collected from ten rose ...
Hala M. Abdelmigid +7 more
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Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges [PDF]
arXiv, 2023 With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application domains, spanning Earth sciences, agriculture, smart cities, and public safety.
Jiang, Zhe
arxiv +2 more sources
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities [PDF]
arXiv, 2023 With the advancement of GPS, remote sensing, and computational simulations, large amounts of geospatial and spatiotemporal data are being collected at an increasing speed. Such emerging spatiotemporal big data assets, together with the recent progress of deep learning technologies, provide unique opportunities to transform society.
Wenchong He, Zhe Jiang
arxiv +3 more sources
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL agent's action space (corresponding
Sambit Ghadai +4 more
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Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data [PDF]
arXiv, 2023 Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data.
Cham, Mostafa +5 more
arxiv +2 more sources
Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020 Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in SR-US, such as singular value decomposition (SVD), are computationally burdensome and performed offline.
Katherine G. Brown +2 more
openaire +4 more sources
DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning [PDF]
Nature Methods, 2022 AbstractSingle molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques depend on accumulation of many localizations over thousands of recorded frames to yield a single super-resolved image, which is time consuming. Hence,
Alon Saguy +5 more
openaire +3 more sources