The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 111–116, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-111-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 111–116, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-111-2021

  30 Jun 2021

30 Jun 2021

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

A. A. Kolesnikov1, P. M. Kikin2, E. A. Panidi2, and A. G. Rusina3 A. A. Kolesnikov et al.
  • 1Siberian State University of Geosystems and Technologies, Novosibirsk, Russian Federation
  • 2Dept. of Geomatic Engineering Engineering, Saint Petersburg State University, St.Petersburg, Russian Federation
  • 3Novosibirsk State Technical University, Novosibirsk, Russian Federation

Keywords: Distributed DBMS, Distributed Processing, Remote Sensing, Raster Data, Climatic 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.