Results 281 to 290 of about 84,629 (319)

Spatiotemporal deep learning

As spatiotemporal sensors become cheaper, spatiotemporal data become more widespread. At the same time, deep learning continues to be the de facto method to extract good representation in multiple applications domains. However, there are several challenges specific to spatiotemporal data.
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Deep learning with applications for spatiotemporal prediction

Spatiotemporal prediction has garnered significant attention for many years. In recent years, deep learning methods have emerged as effective models for spatiotemporal data, surpassing traditional methods in tasks such as data enhancement and prediction. While considerable effort has been dedicated to developing deep learning methods for spatiotemporal
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Deep Spatiotemporal Feature Learning with Application to Image Classification

2010 Ninth International Conference on Machine Learning and Applications, 2010
Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is ...
Thomas P. Karnowski, Itamar Arel, Derek Rose   +2 more
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Real-Time Traffic Prediction Using Deep Spatiotemporal Learning

International Scientific Journal of Engineering and Management
Abstract This study explores the use of deep spatiotemporal learning for real-time traffic prediction. Traditional traffic forecasting methods often rely on historical averages and fail to capture complex spatial and temporal dependencies. This research applies Graph Convolutional Networks (GCN) to model spatial relationships between roads and Long ...
Payal Wani, Shivanjali Yadav.
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Exploring deep learning architectures for spatiotemporal sequence forecasting

2019
Spatiotemporal systems are common in the real world. Forecasting the multi-step future of these spatiotemporal systems based on past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Due to the complex spatial and temporal relationships within the data and the potential long forecast horizon, it is ...
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Using Deep Convolutional LSTM Networks for Learning Spatiotemporal Features

2020
This paper explores the use of convolutional LSTMs to simultaneously learn spatial- and temporal-information in videos. A deep network of convolutional LSTMs allows the model to access the entire range of temporal information at all spatial scales. We describe our experiments involving convolutional LSTMs for lipreading that demonstrate the model is ...
Logan Courtney, Ramavarapu Sreenivas
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Enabling smart curb management with spatiotemporal deep learning

Computers, Environment and Urban Systems, 2023
Haiyan Hao, Yan Wang, Lili Du, Shigang Chen   +3 more
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Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques

For weather forecasters and hydrologists, predicting rainfall in the short term – minutes to a few hours – is crucial for a range of applications. While traditional nowcasting methods excel in operational settings, they face limitations in predicting convective storm formation and high-intensity events.
Ahmed Abdelhalim, Miguel Rico-Ramirez, Weiru Liu, Dawei Han   +3 more
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Physics-Guided Deep Learning for Spatiotemporal Forecasting

2022
Rui Wang, Robin Walters, Rose Yu
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Ultrafast imaging using spatiotemporal encoding and deep learning

Ultrafast Nonlinear Imaging and Spectroscopy XI, 2023
Chen Zhou, Lidan Zhang, Tunan Xia, Cheng-Yu Wang, Xingjie Ni, Zhiwen Liu   +5 more
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