Results 31 to 40 of about 3,246,502 (304)

Dynamic change, driving mechanism and spatiotemporal prediction of the normalized vegetation index: a case study from Yunnan Province, China

Frontiers in Ecology and Evolution, 2023
Vegetation indexes have been widely used to qualitatively and quantitatively evaluate vegetation cover and its growth vigor. To further extend the study of vegetation indexes, this paper proposes to study the spatial and temporal distribution ...
Yang Han, Yilin Lin, P. Zhou, Jinjiang Duan, Zhaoxiang Cao   +4 more
semanticscholar   +1 more source

Probabilistic tracking of motion boundaries with spatiotemporal predictions [PDF]

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2005
We describe a probabilistic framework for detecting and tracking motion boundaries. It builds on previous work (M.J. Black and D.J. Fleet, 2000) that used a particle filter to compute a posterior distribution over multiple, local motion models, one of which was specific for motion boundaries.
Oscar Nestares, David J. Fleet
openaire   +1 more source

Population Distribution Forecasting Based on the Fusion of Spatiotemporal Basic and External Features: A Case Study of Lujiazui Financial District

ISPRS International Journal of Geo-Information
Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning.
Xianzhou Cheng, Xiaoming Wang, Renhe Jiang   +2 more
doaj   +1 more source

Temporal-Spatial Traffic Flow Prediction Model Based on Prompt Learning

ISPRS International Journal of Geo-Information
Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics.
Siteng Cai, Gang Liu, Jing He, Yulun Du, Zhichao Si, Yunhao Jiang   +5 more
doaj   +1 more source

Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

International Journal of Distributed Sensor Networks, 2017
Short-term traffic flow forecasting is a difficult yet important problem in intelligent transportation systems. Complex spatiotemporal interactions between the target road segment and other road segments can provide important information for the accurate
Xiaobo Chen, Zhongjie Wei, Xiang Liu, Yingfeng Cai, Zuoyong Li, Feng Zhao   +5 more
doaj   +1 more source

Correction: Spatiotemporal Patterns and Predictability of Cyberattacks

PLOS ONE, 2015
The following information is missing from the Funding section: This study was also supported by the NSF of China, Grant No. 11275003.
Yu-Zhong Chen, Zi-Gang Huang, Shouhuai Xu, Ying-Cheng Lai   +3 more
openaire   +3 more sources

PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics

Sensors
With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological
Feng Gao, Sen Li, Yuankang Ye, Chang Liu
doaj   +1 more source

A Spatiotemporal Coupling Calculation-Based Short-Term Wind Farm Cluster Power Prediction Method

IEEE Access, 2023
Accurate short-term wind power prediction is of great significance to the real-time dispatching of power systems and the development of wind power generation plans.
Haochen Li, Liqun Liu, Qiusheng He
doaj   +1 more source

Sensitivity to Spatiotemporal Percepts Predicts the Perception of Emotion [PDF]

Journal of Nonverbal Behavior, 2015
The present studies examined how sensitivity to spatiotemporal percepts such as rhythm, angularity, configuration, and force predicts accuracy in perceiving emotion. In Study 1, participants (N = 99) completed a nonverbal test battery consisting of three nonverbal emotion perception tests and two perceptual sensitivity tasks assessing rhythm ...
Vanessa L, Castro, R Thomas, Boone
openaire   +2 more sources

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks [PDF]

SIGSPATIAL/GIS
Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the inherent uncertainty in such prediction.
Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao   +4 more
semanticscholar   +1 more source