Using systems of parallel and distributed data processing to build hydrological models based on remote sensing data

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Using systems of parallel and distributed data processing to build hydrological models based on remote sensing data. / Kolesnikov, A. A.; Kikin, P. M.; Panidi, E. A.; Rusina, A. G.

в: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Том 43, № B4-2021, 30.06.2021, стр. 111-116.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Author

Kolesnikov, A. A. ; Kikin, P. M. ; Panidi, E. A. ; Rusina, A. G. / Using systems of parallel and distributed data processing to build hydrological models based on remote sensing data. в: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2021 ; Том 43, № B4-2021. стр. 111-116.

BibTeX

@article{4ee3332418eb48179a9c89af4d3bca47,

title = "Using systems of parallel and distributed data processing to build hydrological models based on remote sensing data",

abstract = "The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.",

keywords = "Climatic Data, Distributed DBMS, Distributed Processing, Raster Data, Remote Sensing",

author = "Kolesnikov, {A. A.} and Kikin, {P. M.} and Panidi, {E. A.} and Rusina, {A. G.}",

note = "Publisher Copyright: {\textcopyright} Author(s) 2021. CC BY 4.0 License.All right reserved.; 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV ; Conference date: 05-07-2021 Through 09-07-2021",

year = "2021",

month = jun,

day = "30",

doi = "10.5194/isprs-archives-XLIII-B4-2021-111-2021",

language = "English",

volume = "43",

pages = "111--116",

journal = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

issn = "1682-1750",

publisher = "International Society for Photogrammetry and Remote Sensing",

number = "B4-2021",

}

RIS

TY - JOUR

T1 - Using systems of parallel and distributed data processing to build hydrological models based on remote sensing data

AU - Kolesnikov, A. A.

AU - Kikin, P. M.

AU - Panidi, E. A.

AU - Rusina, A. G.

PY - 2021/6/30

Y1 - 2021/6/30

N2 - The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

AB - The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

KW - Climatic Data

KW - Distributed DBMS

KW - Distributed Processing

KW - Raster Data

KW - Remote Sensing

UR - http://www.scopus.com/inward/record.url?scp=85117851762&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLIII-B4-2021-111-2021

DO - 10.5194/isprs-archives-XLIII-B4-2021-111-2021

M3 - Conference article

AN - SCOPUS:85117851762

VL - 43

SP - 111

EP - 116

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

IS - B4-2021

T2 - 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV

Y2 - 5 July 2021 through 9 July 2021

ER -

ID: 87854204