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AHN4_tree-detected.png
AHN4_tree-detected.png
 
 
IR_Rezaul_bhuiyan_s3003337.pdf
IR_Rezaul_bhuiyan_s3003337.pdf
 
 
K_10_tree-detected.png
K_10_tree-detected.png
 
 
Raw_AHN.png
Raw_AHN.png
 
 
Raw_k10.png
Raw_k10.png
 
 
Raw_point_cloud.png
Raw_point_cloud.png
 
 
Reclass_k10.png
Reclass_k10.png
 
 
__init__.py
__init__.py
 
 
abstract_processor.py
abstract_processor.py
 
 
ahn_fuser.py
ahn_fuser.py
 
 
alpha_shape_utils.py
alpha_shape_utils.py
 
 
alternative.ipynb
alternative.ipynb
 
 
building_fuser.py
building_fuser.py
 
 
cable_fuser.py
cable_fuser.py
 
 
car_fuser.py
car_fuser.py
 
 
crwon.png
crwon.png
 
 
crwon_m.png
crwon_m.png
 
 
d.png
d.png
 
 
g.png
g.png
 
 
geotiff_utils.py
geotiff_utils.py
 
 
getimagev2.jpg
getimagev2.jpg
 
 
helper_functions.py
helper_functions.py
 
 
label_connected_comp.py
label_connected_comp.py
 
 
labels.py
labels.py
 
 
layer_lcc.py
layer_lcc.py
 
 
method.png
method.png
 
 
misslcasss_k10.png
misslcasss_k10.png
 
 
noise_filter.py
noise_filter.py
 
 
pipeline.py
pipeline.py
 
 
pole_fuser.py
pole_fuser.py
 
 
region_growing.py
region_growing.py
 
 
road_fuser.py
road_fuser.py
 
 
street_furniture_fuser.py
street_furniture_fuser.py
 
 
trunk_utils.py
trunk_utils.py
 
 

About

This project estimates tree crown volumes using 3D modeling and high-resolution LiDAR datasets (AHN4 and Kavel_10) in Nijmegen, Netherlands. The study focuses on tree detection, biophysical parameter estimation, and crown volume mapping, highlighting applications in urban green space management and ecosystem services. Tools: Python, LiDAR, GIS soft

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python scikit-learn jupyter-notebook machile-learning urban-greenspace open3d-python k-d-tree
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