USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA

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 wil...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	A. A. Kolesnikov [verfasserIn] P. M. Kikin [verfasserIn] E. A. Panidi [verfasserIn] A. G. Rusina [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - Copernicus Publications, 2015, (2021), Seite 111-116
Übergeordnetes Werk:	year:2021 ; pages:111-116

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/isprs-archives-XLIII-B4-2021-111-2021

Katalog-ID:	DOAJ055558259

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spellingShingle	A. A. Kolesnikov misc TA1-2040 misc TA1501-1820 misc Technology misc T misc Engineering (General). Civil engineering (General) misc Applied optics. Photonics USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA
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title	USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA
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title_sort	using systems of parallel and distributed data processing to build hydrological models based on remote sensing data
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title_auth	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA
url	https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-111-2021 https://doaj.org/article/c51969823e974ff3ba52077aea8a4d95 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2021/111/2021/isprs-archives-XLIII-B4-2021-111-2021.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034
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