<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20240709</CreaDate>
<CreaTime>16283700</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>Northland Wetland Mapping Project 2024</resTitle>
</idCitation>
<idAbs/>
<searchKeys>
<keyword>Northland</keyword>
<keyword>Wetlands</keyword>
<keyword>Swamps</keyword>
<keyword>Machine learning</keyword>
</searchKeys>
<idPurp>This machine learning dataset predicts the extent of wetlands defined by the national policy statement for freshwater management. Additional to wetlands as defined by NPSFM, areas suitable for wetland restoration are identified for consideration in developing farm environment plans to trap sediment </idPurp>
<idCredit/>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>
