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This machine learning dataset predicts the extent of gumlands within Northland region. |
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This machine learning dataset predicts the extent of gumlands within Northland region. |
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Biospatial Limited Photoblique Limited Auckland Council Northland Regional Council |
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5000 |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Detailed information about the data, including but not limited to: </SPAN></P><UL><LI><P><SPAN>Gumlands were mapped using a combination of LiDAR (2018) and RGBI (2014-2016) imagery captured by Northland and Auckland Councils. Gumlands were validated using a combination of Photoblique and Biospatial_KMR Historic Imagery layer derived from retrolens. </SPAN></P></LI><LI><P><SPAN>Slope, Spectral signatures, Topographic wetness index and vegetation structure were inputs into the machine learning model. </SPAN></P></LI><LI><P><SPAN>Information is current to the datasets available. </SPAN></P></LI><LI><P><SPAN>Data was derived using a combination of AI model training and manual output validation. </SPAN></P></LI><LI><P><SPAN>Data is presented using a polygon approach for ease of display. </SPAN></P></LI><LI><P><SPAN>Elevation data has been attached for data overlay in Photoblique</SPAN></P><P><SPAN /></P></LI></UL><P><SPAN>Data Limitations: </SPAN></P><UL><LI><P><SPAN> Data may be subject to false positive and/or false negative results. </SPAN></P></LI><LI><P><SPAN>The spatial extents can be used as a guide for where saltmarsh exists </SPAN></P></LI><LI><P><SPAN>Scale is created to a 1m resolution </SPAN></P></LI><LI><P><SPAN>Data is representation of the capture date of the imagery and LiDAR. </SPAN></P></LI></UL><P><SPAN>Version Date: 2024/04/08</SPAN></P></DIV></DIV></DIV> |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Northland Regional Council and Biospatial Limited maintain ownership of the dataset. </SPAN></P></DIV></DIV></DIV> |
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| title:
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Biospatial_NothlandGumlandPolygons_Nrth20240408 |
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| tags:
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["Northland","gumlands","Wetlands","Machine learning"] |
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en-US |
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150000000 |
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