Processing Remote-Sensing Data in Cloud Computing Environments

2018 ◽  
pp. 587-596
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Computing Environments

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.

Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

2018 ◽  
Vol 56 (4) ◽  
pp. 536-553 ◽  
Author(s):  
R. R. Antunes ◽  
T. Blaschke ◽  
D. Tiede ◽  
E. S. Bias ◽  
G. A. O. P. Costa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Proof Of Concept ◽  
Sensing Data ◽  
Object Based ◽  
Computing Approach

A New Cloud Computing Architecture for the Classification of Remote Sensing Data

2017 ◽  
Vol 10 (2) ◽  
pp. 409-416 ◽  
Author(s):  
Victor Andres Ayma Quirita ◽  
Gilson Alexandre Ostwald Pedro da Costa ◽  
Patrick Nigri Happ ◽  
Raul Queiroz Feitosa ◽  
Rodrigo da Silva Ferreira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Remote Sensing Data ◽  
Computing Architecture ◽  
Sensing Data
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