Efficient Spatio-temporal Information Fusion in Sensor Networks

2013 ◽  
Author(s):  
Brijesh Kashyap Chejerla ◽  
Sanjay K. Madria
Keyword(s):  
Sensor Networks ◽  
Information Fusion ◽  
Temporal Information ◽  
Spatio Temporal

Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network

2020 ◽  
Vol 117 ◽  
pp. 102665 ◽  
Author(s):  
Guangyin Jin ◽  
Yan Cui ◽  
Liang Zeng ◽  
Hanbo Tang ◽  
Yanghe Feng ◽  
...  
Keyword(s):  
Information Fusion ◽  
Temporal Information ◽  
Demand Prediction ◽  
Spatio Temporal

scholarly journals UFSP-Net: a neural network with spatio-temporal information fusion for urban fire situation prediction

2020 ◽  
Vol 853 ◽  
pp. 012050
Author(s):  
Guangyin Jin ◽  
Cunchao Zhu ◽  
Xiaoxuan Chen ◽  
Hengyu Sha ◽  
Xingchen Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Temporal Information ◽  
Urban Fire ◽  
Spatio Temporal

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

MLS: An MAE-aware LiDAR sampling framework for On-road Environments using Spatio-Temporal Information

2021 ◽  
pp. 1-1
Author(s):  
Quan-Dung Pham ◽  
Xuan Truong Nguyen ◽  
Khac-Thai Nguyen ◽  
Hyun Kim ◽  
Hyuk-Jae Lee
Keyword(s):  
Temporal Information ◽  
Spatio Temporal
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