20210601 19280500 1.0 FALSE S13_1_2.tif 002 1320.179800 1709.937800 -1321.683300 -899.291300 1 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) Astana <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.4'><WKT>PROJCS[&quot;Astana&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Local&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Scale_Factor&quot;,1.00006382434499],PARAMETER[&quot;Azimuth&quot;,-0.451110818],PARAMETER[&quot;Longitude_Of_Center&quot;,71.42142501666659],PARAMETER[&quot;Latitude_Of_Center&quot;,51.16971010555549],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-6378500</XOrigin><YOrigin>-6349400</YOrigin><XYScale>706037221.9057951</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem> 8 FALSE LZW 3 TIFF TRUE continuous unsigned integer 0 20210604 14380900 20210604 14380900 Version 6.2 (Build 9200) ; Esri ArcGIS 10.4.1.5686 S13_1_2.tif 1 71.440397 71.446013 51.161746 51.157923 Ortho_2 2D ortho Ortho_2 Набор растровых данных dataset 2 1 0 1320.179800 -1321.683300 1320.179800 -899.291300 1709.937800 -899.291300 1709.937800 -1321.683300 1515.058800 -1110.487300 10534 0.037000 11416 0.037000 Red 255.000000 1.000000 8 Green 255.000000 1.000000 8 Blue 255.000000 1.000000 8 20210604 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