scholarly journals Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

2017 ◽  
Vol 9 (11) ◽  
pp. 1105 ◽  
Author(s):  
Uttam Kumar ◽  
Sangram Ganguly ◽  
Ramakrishna R. Nemani ◽  
Kumar S Raja ◽  
Cristina Milesi ◽  
...  
Keyword(s):  
Land Cover ◽  
High Performance Computing ◽  
Satellite Data ◽  
High Performance ◽  
Learning Algorithms ◽  
Global Land Cover ◽  
Global Land ◽  
Performance Computing

A scale-up method for reference data for validation of global land cover maps using ALOS/AVNIR-2 satellite data

2015 ◽  
Author(s):  
Noriko Soyama ◽  
Kanako Muramatsu ◽  
Itsuko Ohashi ◽  
Motomasa Daigo ◽  
Fumio Ochiai ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Scale Up ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps

High performance computing algorithms for land cover dynamics using remote sensing data

2000 ◽  
Vol 21 (6-7) ◽  
pp. 1513-1536 ◽  
Author(s):  
S. N. V. Kalluri ◽  
J. Jaja ◽  
D. A. Bader ◽  
Z. Zhang ◽  
J. R. G. Townshend ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
High Performance Computing ◽  
High Performance ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Land Cover Dynamics ◽  
Performance Computing

scholarly journals High performance computing for machine learning

2021 ◽  
Vol 13(62) (2) ◽  
pp. 705-714
Author(s):  
Arpad Kerestely
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
High Performance ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Computing System ◽  
Machine Learning Algorithms ◽  
Storage Space ◽  
The Past ◽  
Performance Computing

Efficient High Performance Computing for Machine Learning has become a necessity in the past few years. Data is growing exponentially in domains like healthcare, government, economics and with the development of IoT, smartphones and gadgets. This big volume of data, needs a storage space which no traditional computing system can offer, and needs to be fed to Machine Learning algorithms so useful information can be extracted out of it. The larger the dataset that is fed to a Machine Learning algorithm the more precise the results will be, but also the time to compute those results will increase. Thus, the need for Efficient High Performance computing in the aid of faster and better Machine Learning algorithms. This paper aims to unveil how one benefits from another, what research has achieved so far and where is it heading.

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