Toward Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation,
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …
A hybrid data-driven framework for spatiotemporal traffic flow data imputation
An accurate estimation of missing data in traffic flow is crucial in urban planning, intelligent
transportation, economic geography, and other fields. Thus, improving the data quality of …
transportation, economic geography, and other fields. Thus, improving the data quality of …
Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data
Understanding human mobility is significant in many fields, such as geography,
transportation, and sociology. Due to the wide spatiotemporal coverage and low operational …
transportation, and sociology. Due to the wide spatiotemporal coverage and low operational …
[PDF][PDF] 多模态地理大数据时空分析方法
邓敏， 蔡建南， 杨文涛， 唐建波， 杨学习， 刘启亮… - 地球信息科学 …, 2020 - researching.cn
多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律,
异常表现, 关联模式与变化趋势, 是全空间信息系统的核心研究内容, 并有望成为推进地理学人地 …
异常表现, 关联模式与变化趋势, 是全空间信息系统的核心研究内容, 并有望成为推进地理学人地 …
A two-step method for missing spatio-temporal data reconstruction
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal
data; however, few studies comprehensively consider missing data patterns, sample …
data; however, few studies comprehensively consider missing data patterns, sample …
[HTML][HTML] 尺度驱动的空间聚类理论
李志林， 刘启亮， 唐建波 - 2017 - html.rhhz.net
空间聚类是探索性空间数据分析的有力手段, 不仅可以直接用于发现地理现象的分布格局与分布
特征, 亦可以为其他空间数据分析任务提供重要的预处理步骤. 空间聚类有望成为大数据认知的 …
特征, 亦可以为其他空间数据分析任务提供重要的预处理步骤. 空间聚类有望成为大数据认知的 …
Spatiotemporal reconstruction of land surface temperature derived from fengyun geostationary satellite data
The FengYun-2F (FY-2F) geostationary satellite land surface temperature (LST) and its
diurnal variation are important when evaluating climate change, the land-atmosphere …
diurnal variation are important when evaluating climate change, the land-atmosphere …
Heterogeneous space–time artificial neural networks for space–time series prediction
M Deng, W Yang, Q Liu, R Jin, F Xu… - Transactions in …, 2018 - Wiley Online Library
Abstract Space–time series prediction plays a key role in the domain of geographic data
mining and knowledge discovery. In general, the existing methods of space–time series …
mining and knowledge discovery. In general, the existing methods of space–time series …
[PDF][PDF] 异质稀疏分布时空数据插值, 重构与预测方法探讨
程诗奋， 彭澎， 张恒才， 陆锋 - 武汉大学学报(信息科学版), 2020 - sssampling.cn
时空数据挖掘是地理信息科学的核心研究命题. 大数据时代, 地理时空数据的爆炸性增长对时空
知识发现提出了迫切的需求, 促进了时空数据挖掘技术不断发展. 然而, 时空大数据普遍存在的异 …
知识发现提出了迫切的需求, 促进了时空数据挖掘技术不断发展. 然而, 时空大数据普遍存在的异 …
A lightweight ensemble spatiotemporal interpolation model for geospatial data
Missing data is a common problem in the analysis of geospatial information. Existing
methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease …
methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease …