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Fix typo in tutorial
1. part 4 (Generating landscape predictions) --> part 8 2. condidence --> confidence
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docs/source/tutorial.rst

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@@ -39,8 +39,8 @@ below for an example of the required input crowns and image.
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If you would just like to make predictions on an orthomosaic with a pre-trained
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model from the ``model_garden``, skip to part 4 (Generating landscape
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predictions).
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model from the ``model_garden``, skip to `part 2.8 (Generating landscape
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predictions) <https://patball1.github.io/detectree2/tutorial.html#generating-landscape-predictions>`_.
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The data preparation and training process for both RGB and multispectral data
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is presented here. The process is similar for both data types but there are
@@ -727,8 +727,8 @@ predictions with lower confidence).
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crowns = stitch_crowns(tiles_path + "predictions_geo/", 1)
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clean = clean_crowns(crowns, 0.6, confidence=0) # set a confidence>0 to filter out less confident crowns
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By default the ``clean_crowns`` function will remove crowns with a condidence of less than 20%. The above 'clean' crowns
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includes crowns of all confidence scores (0%-100%) as ``condidence=0``. It is likely that crowns with very low
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By default the ``clean_crowns`` function will remove crowns with a confidence of less than 20%. The above 'clean' crowns
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includes crowns of all confidence scores (0%-100%) as ``confidence=0``. It is likely that crowns with very low
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confidence will be poor quality so it is usually preferable to filter these out. A suitable threshold can be determined
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by eye in QGIS or implemented as single line in Python. ``Confidence_score`` is a column in the ``crowns`` GeoDataFrame
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and is considered a tunable parameter.
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clean.to_file(site_path + "/crowns_out.gpkg")
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View the file in QGIS or ArcGIS to see whether you are satisfied with the results. The first output might not be perfect
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and so tweaking of the above parameters may be necessary to get a satisfactory output.
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and so tweaking of the above parameters may be necessary to get a satisfactory output.

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