System architecture of coastal remote sensing data mining and services based on cloud computing

2017 ◽  
Vol 15 (3/4) ◽  
pp. 267
Author(s):  
Xiujuan Wang ◽  
Lingling Wu ◽  
Xuerong Li
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Cloud Computing ◽  
System Architecture ◽  
Remote Sensing Data ◽  
Sensing Data

Application of the Parallel Spatial Association Rule in Remote Sensing Data Mining

2012 ◽  
Vol 500 ◽  
pp. 598-602
Author(s):  
Jun Ma ◽  
Dong Dong Zhang
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Association Rule ◽  
Spatial Association ◽  
Remote Sensing Data ◽  
Inner Product ◽  
Data Mining Algorithm ◽  
Sensing Data ◽  
The Common ◽  
Frequent Item Sets

Since the remote sensing data are multi-resources and massive, the common data mining algorithm cannot effectively discover the knowledge what people want to know. However, spatial association rule can solve the problem of inefficiency in remote sensing data mining. This paper gives an algorithm to compute the frequent item sets though a method like calculating vectors inner-product. And the algorithm will introduce pruning in the whole running. It reduces the time and resources consumption effectively

Framework for network-level pavement condition assessment using remote sensing data mining

2020 ◽  
Vol 14 (02) ◽  
pp. 1
Author(s):  
Stefanos S. Politis ◽  
Zhanmin Zhang ◽  
Sareh Kouchaki ◽  
Carlos H. Caldas
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Condition Assessment ◽  
Pavement Condition ◽  
Sensing Data ◽  
Pavement Condition Assessment

Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective

2011 ◽  
Vol 262 (8) ◽  
pp. 1597-1607 ◽  
Author(s):  
Carmen Quintano ◽  
Alfonso Fernández-Manso ◽  
Alfred Stein ◽  
Wietske Bijker
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Area Burned ◽  
Mediterranean Countries

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

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