Results 51 to 60 of about 497,808 (317)

Spatiotemporal Deep Learning Network for High-Latitude Ionospheric Phase Scintillation Forecasting

Navigation, 2023
In this paper, we present a spatiotemporal deep learning (STDL) network to conduct binary phase scintillation forecasting at a high-latitude global navigation satellite systems (GNSS) station.
Yunxiang Liu, Zhe Yang, Y. Jade Morton, Ruoyu Li   +3 more
doaj   +1 more source

Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach [PDF]

arXiv, 2023
Effective management of environmental resources and agricultural sustainability heavily depends on accurate soil moisture data. However, datasets like the SMAP/Sentinel-1 soil moisture product often contain missing values across their spatiotemporal grid, which poses a significant challenge.
arxiv  

A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences

Journal of Traffic and Transportation Engineering (English ed. Online), 2021
Short-term taxi demand forecasting is of great importance to incentivize vacant cars moving from over-supply regions to over-demand regions, which can minimize the wait time for passengers and drivers. With the consideration of spatiotemporal dependences,
Huimin Luo, Jianming Cai, Kunpeng Zhang, Ruihang Xie, Liang Zheng   +4 more
doaj  

ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation [PDF]

, 2023
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to data-driven solutions. Existing imputation solutions mainly include low-rank models and deep learning models.
arxiv   +1 more source

Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability

Computers in Biology and Medicine, 2021
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis.
Daniela Baumgartner, Theresa Margarethe Rienmüller, Christian Baumgartner, Karen Andrea Lara Hernandez   +3 more
openaire   +3 more sources

Deep Spatiotemporal Attention Network for Fine Particle Matter 2.5 Concentration Prediction With Causality Analysis

IEEE Access, 2021
The increasing concentration of air pollutants, caused by industrialization and economic growth, is adversely affecting public health. Therefore, accurately measuring and predicting air pollution has been an important societal issue.
Nohyoon Seong
doaj   +1 more source

Mitigating Imbalance of Land Cover Change Data for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting Schemes

ISPRS International Journal of Geo-Information, 2022
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to ...
Alysha van Duynhoven, Suzana Dragićević   +1 more
doaj   +1 more source

CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect cloud forecasts can lead to major uncertainty in the overall accuracy of weather forecasts due to their intrinsic role in the Earth&#
Andreas Holm Nielsen, Alexandros Iosifidis, Henrik Karstoft   +2 more
doaj   +1 more source

Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction

, 2019
Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules.
Bayoumi, Magdy, Elsayed, Nelly, Maida, Anthony S.   +2 more
core   +1 more source

Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning

Virtual and Physical Prototyping
Additive manufacturing offers a solution for producing advanced ceramics with complex geometries by enabling precise control over geometry, microstructure, and composition.
Mohammad Rezasefat, James D. Hogan
doaj   +1 more source