diff --git a/python_scripts/add_trees_to_open_street_map/add_trees.py b/python_scripts/add_trees_to_open_street_map/add_trees.py
index 4038f0e0c9d32dc6dc5e4477ae02c95d5b0253d9..a1096a0143d49fc23090bc7a155ff9c21b01e3d9 100644
--- a/python_scripts/add_trees_to_open_street_map/add_trees.py
+++ b/python_scripts/add_trees_to_open_street_map/add_trees.py
@@ -2,6 +2,7 @@
 Script to automatically add potential trees in a given region, along roads and paths.
 
 Road information is downloaded from OpenStreetMap.
+Existing trees can be imported from shapefiles if desired
 
 Trees are exported in a CSV table, a PNG diagram and an HTML interactive map.
 """
@@ -27,15 +28,12 @@ from import_existing_trees import get_existing_forest
 # TODO: Document
 # TODO: Write issue
 # TODO: Write tests?
-# TODO: Export shapefile?
 
 # From RegionChooser, or https://transfer.hft-stuttgart.de/gitlab/circulargreensimcity/circulargreensimcity/-/wikis/Fallstudien/Gromb%C3%BChl
 WKT = "POLYGON((9.947021 49.803063, 9.947011 49.800917, 9.955025 49.800810, 9.955110 49.803019, 9.947021 49.803063))"
 # Replace with None if no existing tree should be imported
 EXISTING_TREES = 'existing_trees/Trees_ideal_2_20240227.shp'
 # EXISTING_TREES = 'existing_trees/baumkataster/Baum.shp'
-# Fellbach
-# WKT = "POLYGON((9.271353 48.811327, 9.271911 48.809010, 9.272147 48.807187, 9.275838 48.807173, 9.275602 48.806749, 9.276138 48.806325, 9.277683 48.806424, 9.277319 48.812514, 9.275581 48.811991, 9.271353 48.811327))"
 EPSG_ID = 25832
 # Trees will be planted every TREE_DISTANCE along roads:
 TREE_DISTANCE = 10  # [m]
@@ -75,7 +73,7 @@ def get_osm_roads(bounds):
             ways = pickle.load(cache)
     else:
         print("Downloading data...")
-        # TODO: Could add trees from OSM or Bäumekataster too.
+        # TODO: Could add trees from OSM too.
         api = overpy.Overpass()
         result = api.query(f"""
         way({bounds.S},{bounds.W},{bounds.N},{bounds.E}) ["highway"];
@@ -221,7 +219,7 @@ def export_shapefile(bounds: Bounds, forest: Forest, tree_distance: float, epsg_
         'Bezeichnun': t.description, 'Baumart': t.type,
         'Baumhöhe': t.height, 'Kronenbrei': t.diameter,
         'Stammumfan': t.trunk_diameter, 'Quelle': t.source
-        }
+    }
         for t in forest]
 
     df = pd.DataFrame.from_dict(data)