modeling module¶
surface_models(laz_fp, outlas='', outtif='', user_dem='', dem_low=20, dem_high=50, mean_k=20, multiplier=3, lidar_pc='yes')
¶
Use filters.dem, filters.mongo, filters.elm, filters.outlier, filters.smrf, and filters.range to filter the point cloud for surface models.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
laz_fp |
_type_ |
description |
required |
outlas |
str |
description. Defaults to ''. |
'' |
outtif |
str |
description. Defaults to ''. |
'' |
user_dem |
str |
description. Defaults to ''. |
'' |
dem_low |
int |
description. Defaults to 20. |
20 |
dem_high |
int |
description. Defaults to 50. |
50 |
mean_k |
int |
description. Defaults to 20. |
20 |
multiplier |
int |
description. Defaults to 3. |
3 |
Returns:
| Type | Description |
|---|---|
_type_ |
Filepath to the terrain model. |
Source code in snow_pc/modeling.py
def surface_models(laz_fp, outlas = '', outtif = '', user_dem = '', dem_low = 20, dem_high = 50, mean_k = 20, multiplier = 3, lidar_pc = 'yes'):
"""Use filters.dem, filters.mongo, filters.elm, filters.outlier, filters.smrf, and filters.range to filter the point cloud for surface models.
Args:
laz_fp (_type_): _description_
outlas (str, optional): _description_. Defaults to ''.
outtif (str, optional): _description_. Defaults to ''.
user_dem (str, optional): _description_. Defaults to ''.
dem_low (int, optional): _description_. Defaults to 20.
dem_high (int, optional): _description_. Defaults to 50.
mean_k (int, optional): _description_. Defaults to 20.
multiplier (int, optional): _description_. Defaults to 3.
Returns:
_type_: Filepath to the terrain model.
"""
#set the working directory
in_dir = os.path.dirname(laz_fp)
# os.chdir(in_dir)
#create a filepath for the output las and tif file
if outlas == '':
outlas = join(in_dir, 'dsm.laz')
if outtif == '':
outtif = join(in_dir, 'dsm.tif')
#set dem_fp
dem_fp = join(in_dir, 'dem.tif')
#download dem using download_dem() if user_dem is not provided
if user_dem == '':
dem_fp, crs, project = download_dem(laz_fp, dem_fp= dem_fp)
else:
shutil.copy(user_dem, dem_fp) #if user_dem is provided, copy the user_dem to dem_fp
if lidar_pc.lower() == 'yes':
#create a json pipeline for pdal
json_pipeline = {
"pipeline": [
{
"type": "readers.las",
"filename": laz_fp
},
{
"type": "filters.dem",
"raster": dem_fp,
"limits": f"Z[{dem_low}:{dem_high}]"
},
{
"type": "filters.mongo",\
"expression": {"$and": [\
{"ReturnNumber": {"$gt": 0}},\
{"NumberOfReturns": {"$gt": 0}} ] }
},
{
"type": "filters.elm"
},
{
"type": "filters.outlier",\
"method": "statistical",\
"mean_k": mean_k,\
"multiplier": multiplier
},
{"type": "filters.range",\
"limits":"returnnumber[1:1]"
},
{
"type": "writers.las",
"filename": outlas,
"major_version": 1,
"minor_version": 4
},
{
"type": "writers.gdal",
"filename": outtif,
"resolution": 1.0,
"output_type": "idw"
}
]
}
else:
#create a json pipeline for pdal
json_pipeline = {
"pipeline": [
{
"type": "readers.las",
"filename": laz_fp
},
{
"type": "filters.dem",
"raster": dem_fp,
"limits": f"Z[{dem_low}:{dem_high}]"
},
{
"type": "filters.elm"
},
{
"type": "filters.outlier",\
"method": "statistical",\
"mean_k": mean_k,\
"multiplier": multiplier
},
{"type": "filters.range",\
"limits":"Classification[2:6]"
},
{
"type": "writers.las",
"filename": outlas,
"major_version": 1,
"minor_version": 4
},
{
"type": "writers.gdal",
"filename": outtif,
"resolution": 1.0,
"output_type": "idw"
}
]
}
#create a directory to save the json pipeline
json_dir = join(in_dir, 'jsons')
os.makedirs(json_dir, exist_ok= True)
json_name = 'dsm_pipeline'
json_to_use = join(json_dir, f'{json_name}.json')
#write json pipeline to file
with open(json_to_use, 'w') as f:
json.dump(json_pipeline, f)
#run the json pipeline
subprocess.run(["pdal", "pipeline", json_to_use])
return outlas, outtif
terrain_models(laz_fp, outlas='', outtif='', user_dem='', dem_low=20, dem_high=50, mean_k=20, multiplier=3, lidar_pc='yes')
¶
Use filters.dem, filters.mongo, filters.elm, filters.outlier, filters.smrf, and filters.range to filter the point cloud for terrain models.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
laz_fp |
_type_ |
Filepath to the point cloud file. |
required |
outlas |
str |
Filepath to save the output las file. Defaults to ''. |
'' |
outtif |
str |
Filepath to save the output tif file. Defaults to ''. |
'' |
user_dem |
str |
Filepath to the dem file. Defaults to ''. |
'' |
dem_low |
int |
description. Defaults to 20. |
20 |
dem_high |
int |
description. Defaults to 50. |
50 |
mean_k |
int |
description. Defaults to 20. |
20 |
multiplier |
int |
description. Defaults to 3. |
3 |
Returns:
| Type | Description |
|---|---|
_type_ |
Filepath to the terrain model. |
Source code in snow_pc/modeling.py
def terrain_models(laz_fp, outlas = '', outtif = '', user_dem = '', dem_low = 20, dem_high = 50, mean_k = 20, multiplier = 3, lidar_pc = 'yes'):
"""Use filters.dem, filters.mongo, filters.elm, filters.outlier, filters.smrf, and filters.range to filter the point cloud for terrain models.
Args:
laz_fp (_type_): Filepath to the point cloud file.
outlas (str, optional): Filepath to save the output las file. Defaults to ''.
outtif (str, optional): Filepath to save the output tif file. Defaults to ''.
user_dem (str, optional): Filepath to the dem file. Defaults to ''.
dem_low (int, optional): _description_. Defaults to 20.
dem_high (int, optional): _description_. Defaults to 50.
mean_k (int, optional): _description_. Defaults to 20.
multiplier (int, optional): _description_. Defaults to 3.
Returns:
_type_: Filepath to the terrain model.
"""
#set the working directory
in_dir = os.path.dirname(laz_fp)
# os.chdir(in_dir)
#create a filepath for the output las and tif file
if outlas == '':
outlas = join(in_dir, 'dtm.laz')
if outtif == '':
outtif = join(in_dir, 'dtm.tif')
#set dem_fp
dem_fp = join(in_dir, 'dem.tif')
#download dem using download_dem() if user_dem is not provided
if user_dem == '':
dem_fp, crs, project = download_dem(laz_fp, dem_fp= dem_fp)
else:
shutil.copy(user_dem, dem_fp) #if user_dem is provided, copy the user_dem to dem_fp
if lidar_pc.lower() == 'yes':
#create a json pipeline for pdal
json_pipeline = {
"pipeline": [
{
"type": "readers.las",
"filename": laz_fp
},
{
"type": "filters.dem",
"raster": dem_fp,
"limits": f"Z[{dem_low}:{dem_high}]"
},
{
"type": "filters.mongo",\
"expression": {"$and": [\
{"ReturnNumber": {"$gt": 0}},\
{"NumberOfReturns": {"$gt": 0}} ] }
},
{
"type": "filters.elm"
},
{
"type": "filters.outlier",\
"method": "statistical",\
"mean_k": mean_k,\
"multiplier": multiplier
},
{
"type": "filters.smrf",\
"ignore": "Classification[7:7], NumberOfReturns[0:0], ReturnNumber[0:0]"
},
{
"type": "filters.range",
"limits": "Classification[2:2]"
},
{
"type": "writers.las",
"filename": outlas,
"major_version": 1,
"minor_version": 4
},
{
"type": "writers.gdal",
"filename": outtif,
"resolution": 1.0,
"output_type": "idw"
}
]
}
else:
#create a json pipeline for pdal
json_pipeline = {
"pipeline": [
{
"type": "readers.las",
"filename": laz_fp
},
{
"type": "filters.dem",
"raster": dem_fp,
"limits": f"Z[{dem_low}:{dem_high}]"
},
{
"type": "filters.elm"
},
{
"type": "filters.outlier",\
"method": "statistical",\
"mean_k": mean_k,\
"multiplier": multiplier
},
# {
# "type": "filters.smrf",\
# "ignore": "Classification[7:7], NumberOfReturns[0:0], ReturnNumber[0:0]"
# },
{
"type": "filters.range",
"limits": "Classification[2:2]"
},
{
"type": "writers.las",
"filename": outlas,
"major_version": 1,
"minor_version": 4
},
{
"type": "writers.gdal",
"filename": outtif,
"resolution": 1.0,
"output_type": "idw"
}
]
}
#create a directory to save the json pipeline
json_dir = join(in_dir, 'jsons')
os.makedirs(json_dir, exist_ok= True)
json_name = 'dtm_pipeline'
json_to_use = join(json_dir, f'{json_name}.json')
#write json pipeline to file
with open(json_to_use, 'w') as f:
json.dump(json_pipeline, f)
#run the json pipeline
subprocess.run(["pdal", "pipeline", json_to_use])
return outlas, outtif