20150724 08324600 1.0 FALSE Erie2008_2_meter 002 159081.210000 219541.210000 4704815.520000 4778557.520000 1 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_UTM_zone_18N <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/10.3'><WKT>PROJCS[&quot;NAD_1983_UTM_zone_18N&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;false_easting&quot;,500000.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-75.0],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;latitude_of_origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26918]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</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><WKID>26918</WKID><LatestWKID>26918</LatestWKID></ProjectedCoordinateSystem> 32 FALSE LZW 1 TIFF TRUE continuous floating point -3.4028231e+038 20160108 09370900 20160108 09370900 Esri ArcGIS 10.3.1.4959 Erie2008_2_meter 1 -79.188017 -78.410141 43.107829 42.420739 County_Erie2008_2_meter County_Erie2008_2_meter County_Erie2008_2_meter File Geodatabase Raster Dataset dataset EPSG 8.6.2 2 1 0 159081.210000 4704815.520000 159081.210000 4778557.520000 219541.210000 4778557.520000 219541.210000 4704815.520000 189311.210000 4741686.520000 30230 2.000000 36871 2.000000 Band_1 32 20160108 Pixel 36871 30230 2.000000 2.000000 32 1 Upper Left FALSE LZ77 1 pixel codes FGDBR TRUE row and column 2.000000 2.000000 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