Merge pull request #82 from mundialis/ml_schalter

split r.trees.mltrain into r.trees.traindata and r.trees.mltrain

README.md

@@ -39,8 +39,9 @@ Repo for code and script transfer between mundialis and RVR - GRASS GIS addons:
 - **m.analyse.trees**
 
   - **r.trees.peaks** assigns pixels to nearest peak (tree crown).
-  - **r.trees.mltrain** generates training data for a machine learning (ML)
-    model to detect trees and trains the model with these training data.
+  - **r.trees.traindata** generates training data for a machine learning (ML) model
+    to detect trees and provides a preliminray tree candidate map in either vector or raster format.
+  - **r.trees.mltrain** trains the ML model with the training data from before or own training data.
   - **r.trees.mlapply** applies the tree classification model
     in parallel to the area of interest (current region).
   - **r.trees.mlapply.worker** applies classification model for a region

grass-gis-addons/m.analyse.trees/m.analyse.trees.html

@@ -49,5 +49,6 @@ <h2>AUTHORS</h2>
 Momen Mawad, <a href="https://www.mundialis.de/">mundialis</a>, mawad at mundialis.de
 <p>
 Lina Krisztian, <a href="https://www.mundialis.de/">mundialis</a>, krisztian at mundialis.de
-
+<p>Guido Riembauer, <a href="https://www.mundialis.de/">mundialis</a>, riembauer at mundialis.de</p>
+<p>Victoria-Leandra Brunn, <a href="https://www.mundialis.de/">mundialis</a>, brunn at mundialis.de</p>
 

grass-gis-addons/m.analyse.trees/r.trees.mltrain/r.trees.mltrain.html

@@ -22,26 +22,23 @@ <h3>Generation of training data for tree and non-tree classification</h3>
                 ndgb_raster=top.ndgb \
                 ndsm=ndsm \
                 slope=ndsm_slope \
-                nearest=trees_nearest \
-                peaks=trees_peaks \
+                trees_pixel_ndvi=trees_pixel_ndvi \
+                trees_raw_rast=trees_raw_rast \
                 group=ml_input \
-                save_model=ml_trees_randomforest.gz \
-                ndvi_threshold=130 \
-                nir_threshold=130 \
-                ndsm_threshold=1 \
-                slopep75_threshold=70 \
-                area_threshold=5 
+                save_model=ml_trees_randomforest.gz
 </pre></div>
 
 <h2>SEE ALSO</h2>
 
 <em>
 <a href="m.analyse.trees.html">m.analyse.trees</a>,
-<a href="https://grass.osgeo.org/grass-stable/manuals/r.geomorphon.html">r.geomorphon</a>,
 <a href="r.learn.train.html">r.learn.train</a>,
 <a href="r.learn.predict.html">r.learn.predict</a>
 </em>
 
 <h2>AUTHOR</h2>
 
-Markus Metz, <a href="https://www.mundialis.de/">mundialis</a>, metz at mundialis.de
+<p>Markus Metz, <a href="https://www.mundialis.de/">mundialis</a>, metz at mundialis.de</p>
+<p>Lina Krisztian, <a href="https://www.mundialis.de/">mundialis</a>, krisztian at mundialis.de</p>
+<p>Guido Riembauer, <a href="https://www.mundialis.de/">mundialis</a>, riembauer at mundialis.de</p>
+<p>Victoria-Leandra Brunn, <a href="https://www.mundialis.de/">mundialis</a>, brunn at mundialis.de</p>