Possibilities of deep learning neural networks for satellite image recognition
The main problem solved in this project is the analysis of big data using a system of computer processing and recognition of satellite images, based on a deep neural network architecture. The goal of the project is to develop methodological, theoretical and practical aspects of building such systems...
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| Published in | Journal of physics. Conference series Vol. 1703; no. 1; pp. 12031 - 12036 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.12.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1742-6588 1742-6596 |
| DOI | 10.1088/1742-6596/1703/1/012031 |
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| Abstract | The main problem solved in this project is the analysis of big data using a system of computer processing and recognition of satellite images, based on a deep neural network architecture. The goal of the project is to develop methodological, theoretical and practical aspects of building such systems in poorly formalized subject areas, as well as to study the possibilities and advantages of building predictive models for analyzing fresh water reserves and predicting the direction, speed and nature of the spread of large fires using such systems. and assessments of the economic impact of these natural disasters. |
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| AbstractList | The main problem solved in this project is the analysis of big data using a system of computer processing and recognition of satellite images, based on a deep neural network architecture. The goal of the project is to develop methodological, theoretical and practical aspects of building such systems in poorly formalized subject areas, as well as to study the possibilities and advantages of building predictive models for analyzing fresh water reserves and predicting the direction, speed and nature of the spread of large fires using such systems. and assessments of the economic impact of these natural disasters. |
| Author | Averkin, A N Yarushev, S A |
| Author_xml | – sequence: 1 givenname: A N surname: Averkin fullname: Averkin, A N email: averkin2003@inbox.ru organization: Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences – sequence: 2 givenname: S A surname: Yarushev fullname: Yarushev, S A organization: Department of Informatics, Plekhanov Russian University of Economics , Russia |
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| Cites_doi | 10.1016/j.knosys.2008.03.045 10.1093/brain/120.4.701 10.1162/neco.2006.18.7.1527 |
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| Copyright | Published under licence by IOP Publishing Ltd 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1088/1742-6596/1703/1/012031 |
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| References | Ranzato (JPCS_1703_1_012031bib1) 2008 Kohonen (JPCS_1703_1_012031bib6) 2001 Mountcastle (JPCS_1703_1_012031bib5) 1997; 120 Yu (JPCS_1703_1_012031bib4) 2008; 21 Hinton (JPCS_1703_1_012031bib2) 2012; 18 Sabour (JPCS_1703_1_012031bib3) 2017 Averkin (JPCS_1703_1_012031bib7) 2018; 2267 |
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| SubjectTerms | Artificial neural networks Computer architecture Deep learning Economic impact Fresh water Impact analysis Natural disasters Neural networks Object recognition Physics Prediction models Satellite imagery |
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| Title | Possibilities of deep learning neural networks for satellite image recognition |
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