From 8727842b89f310c770b1eef17df7fa7baea72d95 Mon Sep 17 00:00:00 2001
From: Maciej Wielgosz <maciej.wielgosz@nibio.no>
Date: Tue, 16 May 2023 10:49:29 +0200
Subject: [PATCH] updated for better quality of the steep ground in sem seg
---
sean_sem_seg/post_segmentation_script.py | 2 +-
sean_sem_seg/run_single_file.py | 13 +++++++------
2 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/sean_sem_seg/post_segmentation_script.py b/sean_sem_seg/post_segmentation_script.py
index 8a09ab5..cead3ae 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 7961f1a..d0e851a 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
--
GitLab