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A Novel Interpretable Deep Learning Model for Ozone Prediction

Applied Sciences, 2023
Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions.
Xingguo Chen, Yang Li, Xiaoyan Xu, Min Shao   +3 more
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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

A large-scale image–text dataset benchmark for farmland segmentation [PDF]

Earth System Science Data
Understanding and mastering the spatiotemporal characteristics of farmland are essential for accurate farmland segmentation. The traditional deep learning paradigm that solely relies on labeled data has limitations in representing the spatial ...
C. Tao, D. Zhong, W. Mu, Z. Du, H. Wu
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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
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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
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The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spatiotemporal fusion is a method of fusing high spatial resolution low temporal resolution remote sensing images and low spatial resolution high temporal resolution in order to obtain high spatiotemporal resolution remote sensing images, which can ...
ChenYang Weng, Yulin Zhan, Xingfa Gu, Jian Yang, Yan Liu, Hong Guo, Zilong Lian, Shiyuan Zhang, Zhangjie Wang, Xuechun Zhao   +9 more
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A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network

Remote Sensing, 2020
Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors.
Duo Jia, Changqing Song, Changxiu Cheng, Shi Shen, Lixin Ning, Chun Hui   +5 more
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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
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images

Sensors
Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, the number and variety of remote sensing satellites have increased, which provide abundant data for precise ...
Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang   +5 more
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Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change

Remote Sensing, 2022
Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models.
Alysha van Duynhoven, Suzana Dragićević   +1 more
doaj   +1 more source