Intelligent modeling strategies for forecasting air quality time series: A review
In recent years, the deterioration of air quality, the frequent events of the air contaminants,
and the health impacts from that have caused continuous attention by the government and …
and the health impacts from that have caused continuous attention by the government and …
Machine learning of spatial data
B Nikparvar, JC Thill - ISPRS International Journal of Geo-Information, 2021 - mdpi.com
Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …
learning for spatial domains of application. At the same time, resources that would identify …
A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China
Landslides are a type of serious geologic disaster causing great damage to the human
environment. Landslide susceptibility mapping is an effective means to reduce landslide …
environment. Landslide susceptibility mapping is an effective means to reduce landslide …
A new attention-based CNN approach for crop mapping using time series Sentinel-2 images
Accurate crop mapping is of great importance for agricultural applications, and deep
learning methods have been applied on multi-temporal remotely sensed images to classify …
learning methods have been applied on multi-temporal remotely sensed images to classify …
Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks
Dynamic human activity intensity information is of great importance in many location-based
applications. However, two limitations remain in the prediction of human activity intensity …
applications. However, two limitations remain in the prediction of human activity intensity …
Advancing process-oriented geographical regionalization model
Existing regionalization methods have largely overlooked the temporal dimension, leading
to outcomes that predominantly reflect spatial differentiation of regional variables only at a …
to outcomes that predominantly reflect spatial differentiation of regional variables only at a …
A knowledge representation model based on the geographic spatiotemporal process
K Zheng, MH Xie, JB Zhang, J Xie… - International Journal of …, 2022 - Taylor & Francis
Knowledge graphs (KGs) represent entities and relations as computable networks, which is
of great value for discovering hidden knowledge and patterns. Geographic KGs mainly …
of great value for discovering hidden knowledge and patterns. Geographic KGs mainly …
[HTML][HTML] Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities
This paper provides a short overview of space–time series clustering, which can be
generally grouped into three main categories such as: hierarchical, partitioning-based, and …
generally grouped into three main categories such as: hierarchical, partitioning-based, and …
HLSTM: Heterogeneous long short-term memory network for large-scale InSAR ground subsidence prediction
Q Liu, Y Zhang, J Wei, H Wu… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Accurate prediction of ground subsidence is of great significance for the prevention and
mitigation of this type of geological disaster. It is still a challenge when wide area is …
mitigation of this type of geological disaster. It is still a challenge when wide area is …
[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 …