diff --git a/sean_sem_seg/post_segmentation_script.py b/sean_sem_seg/post_segmentation_script.py
index 8a09ab5db9af1718e49c05e112d3aa4b0f06b1f2..cead3ae3e4d37d859fbf9daa278329bf0462e444 100644
--- a/sean_sem_seg/post_segmentation_script.py
+++ b/sean_sem_seg/post_segmentation_script.py
@@ -129,7 +129,7 @@ class PostProcessing:
         self.plot_area = self.convexhull.volume / 10000  # volume is area in 2d.
         print("Plot area is approximately", self.plot_area, "ha")
 
-        above_and_below_DTM_trim_dist = 0.2
+        above_and_below_DTM_trim_dist = 0.5 # meters (BEFORE: 0.2)
 
         self.point_cloud = get_heights_above_DTM(
             self.point_cloud, self.DTM
diff --git a/sean_sem_seg/run_single_file.py b/sean_sem_seg/run_single_file.py
index 7961f1acfe3ba5196306b68b7b72d3814d77223e..d0e851afabfc731a19dd65406325cf375c10f1c1 100644
--- a/sean_sem_seg/run_single_file.py
+++ b/sean_sem_seg/run_single_file.py
@@ -76,6 +76,7 @@ if __name__ == "__main__":
         delete_working_directory=True,  # Generally leave this on. Deletes the files used for segmentation after segmentation is finished.
         # You may wish to turn it off if you want to re-run/modify the segmentation code so you don't need to run pre-processing every time.
         minimise_output_size_mode=0,  # Will delete a number of non-essential outputs to reduce storage use.
+        grid_resolution=0.1,  # Resolution of the grid used for the DTM.
     )
 
     parameters.update(other_parameters)
@@ -88,12 +89,12 @@ if __name__ == "__main__":
         parameters=parameters,
         # Set below to 0 or 1 (or True/False). Each step requires the previous step to have been run already.
         # For standard use, just leave them all set to 1 except "clean_up_files".
-        preprocess=1,  # Preparation for semantic segmentation.
-        segmentation=1,  # Deep learning based semantic segmentation of the point cloud.
-        postprocessing=1,  # Creates the DTM and applies some simple rules to clean up the segmented point cloud.
-        measure_plot=0,  # The bulk of the plot measurement happens here.
-        make_report=0,  # Generates a plot report, plot map, and some other figures.
-        clean_up_files=0,
+        preprocess=True,  # Preparation for semantic segmentation.
+        segmentation=True,  # Deep learning based semantic segmentation of the point cloud.
+        postprocessing=True,  # Creates the DTM and applies some simple rules to clean up the segmented point cloud.
+        measure_plot=False,  # The bulk of the plot measurement happens here.
+        make_report=False,  # Generates a plot report, plot map, and some other figures.
+        clean_up_files=False,
     )  # Optionally deletes most of the large point cloud outputs to minimise storage requirements.
 
     # copy the output "segmented_cleaned.las" to the output directory