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Yong Liu, Xiaohui Yan, Wenying Du, Tianqi Zhang, Xiaopeng Bai and Ruichuan Nan
The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with t...
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Minghao Liu, Qingxi Luo, Jianxiang Wang, Lingbo Sun, Tingting Xu and Enming Wang
Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth?s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining...
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Dong Jiang, Wenji Zhao, Yanhui Wang and Biyu Wan
Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congesti...
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Wen Tian, Yining Zhang, Ying Zhang, Haiyan Chen and Weidong Liu
To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was co...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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Benjamin Burrichter, Julian Hofmann, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview ...
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Dapeng Jiang, Guoyou Shi, Na Li, Lin Ma, Weifeng Li and Jiahui Shi
In the context of the rapid development of deep learning theory, predicting future motion states based on time series sequence data of ship trajectories can significantly improve the safety of the traffic environment. Considering the spatiotemporal corre...
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Yue Zhang, Zimo Zhou, Jesse Van Griensven Thé, Simon X. Yang and Bahram Gharabaghi
Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance...
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Mayra Salcedo-Gonzalez, Julio Suarez-Paez, Manuel Esteve and Carlos Enrique Palau
This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and...
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Yidi Wei, Yongcun Cheng, Xiaobin Yin, Qing Xu, Jiangchen Ke and Xueding Li
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, ...
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