Pavement Analysis API
Module: mls.pavement. This page documents 5 public functions with exact tool IDs, complete parameters, Parameters fields, and examples.
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Functions
onnxdependency_damage_by_cloud — Point cloud-based distress detection - detects 7 geometric distress types (rutting, depression, pothole, bump, upheaval, waves, shoving) using MLS point cloud geometry analysis
onnxdependency_damage_by_img — Image-based distress detection - runs deep learning on panoramic/planar images, back-projects 2D results to 3D; detects 6 types (transverse/longitudinal/alligator crack, pothole, patching, raveling)
onnxdependency_sample_unit_generator — Generates sample units along the road for PCI calculation; supports Auto mode (curb detection via trajectory)
onnxdependency_pci_cal — Computes PCI per ASTM D6433-07 from distress and sample unit data; outputs PCI Section layer
onnxdependency_pci_report — Exports a comprehensive HTML report from the Distress and PCI Section layers
onnxdependency_damage_by_cloud
Point cloud-based distress detection - detects 7 geometric distress types (rutting, depression, pothole, bump, upheaval, waves, shoving) using MLS point cloud geometry analysis
- Exact tool ID:
OnnxDependency_Damage_By_Cloud
- Recommended call:
mls.pavement.onnxdependency_damage_by_cloud(...)
- Alias:
OnnxDependency_Damage_By_Cloud
- Required parameters:
input_paths
Parameters
| Parameter |
Type |
Required |
Default |
Description |
input_paths |
array |
Yes |
— |
Point cloud file paths |
Parameters |
object |
No |
— |
Point cloud distress detection parameters |
Parameters fields
| Field |
Type |
Required |
Default |
Description |
classMask |
object |
No |
{'0': 2} |
The class of ground points in point cloud file |
laneWidth |
number |
No |
3.7 |
Lane width in meters. Defines the width of each lane for road range calculation. |
leftLaneNum |
integer |
No |
1 |
Number of lanes on the left side of the road centerline. |
rightLaneNum |
integer |
No |
1 |
Number of lanes on the right side of the road centerline. |
tolerance |
number |
No |
0.006 |
Elevation tolerance in meters. Points exceeding this height/depth threshold from the road surface are considered potential damage. |
minArea |
number |
No |
0.1 |
Minimum area in m². Damage regions smaller than this are filtered out as noise. |
Example
from gvscript import mls
params = mls.pavement.onnxdependency_damage_by_cloud_parameters()
params.classMask = {'0': 2}
result = mls.pavement.onnxdependency_damage_by_cloud(
Parameters=params,
input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)
onnxdependency_damage_by_img
Image-based distress detection - runs deep learning on panoramic/planar images, back-projects 2D results to 3D; detects 6 types (transverse/longitudinal/alligator crack, pothole, patching, raveling)
- Exact tool ID:
OnnxDependency_Damage_By_Img
- Recommended call:
mls.pavement.onnxdependency_damage_by_img(...)
- Alias:
OnnxDependency_Damage_By_Img
- Required parameters:
input_paths
Parameters
| Parameter |
Type |
Required |
Default |
Description |
input_paths |
array |
Yes |
— |
Point cloud file paths |
Parameters |
object |
No |
— |
Image distress detection parameters |
Parameters fields
| Field |
Type |
Required |
Default |
Description |
classMask |
object |
No |
{'0': 2} |
The class of ground points in point cloud file |
maxDist |
number |
No |
15.0 |
Maximum distance in meters. Damage detected farther than this from the camera is filtered out. |
distRadius |
number |
No |
20.0 |
Projection radius in meters. Controls the distance range for projecting image detection results onto the point cloud. |
minArea |
number |
No |
0.1 |
Minimum area in m². Damage regions smaller than this are filtered out. |
camType |
integer |
No |
0 |
Camera type: 0 = panoramic, 1 = planar |
imgRanges |
object |
No |
— |
Planar camera image ID ranges, keyed by camera ID. |
Example
from gvscript import mls
params = mls.pavement.onnxdependency_damage_by_img_parameters()
params.classMask = {'0': 2}
result = mls.pavement.onnxdependency_damage_by_img(
Parameters=params,
input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)
onnxdependency_sample_unit_generator
Generates sample units along the road for PCI calculation; supports Auto mode (curb detection via trajectory)
- Exact tool ID:
OnnxDependency_Sample_Unit_Generator
- Recommended call:
mls.pavement.onnxdependency_sample_unit_generator(...)
- Alias:
OnnxDependency_Sample_Unit_Generator
- Required parameters:
input_paths
Parameters
| Parameter |
Type |
Required |
Default |
Description |
input_paths |
array |
Yes |
— |
Point cloud file paths |
Parameters |
object |
No |
— |
Sample unit generation parameters |
Parameters fields
| Field |
Type |
Required |
Default |
Description |
classMask |
object |
No |
{'0': 2} |
The class of ground points in point cloud file |
stdArea |
number |
No |
255.0 |
Standard sample unit area in m². Each sample unit will be approximately this size. |
minLaneWidth |
number |
No |
2.5 |
Minimum lane width in meters (AUTO mode). Used to detect the inner road boundary. |
maxLaneWidth |
number |
No |
3.7 |
Maximum lane width in meters (AUTO mode). Used to detect the outer road boundary. |
trajectory |
string |
No |
'' |
Trajectory file path (AUTO mode). The trajectory defines the road alignment direction. |
Example
from gvscript import mls
params = mls.pavement.onnxdependency_sample_unit_generator_parameters()
params.classMask = {'0': 2}
result = mls.pavement.onnxdependency_sample_unit_generator(
Parameters=params,
input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)
onnxdependency_pci_cal
Computes PCI per ASTM D6433-07 from distress and sample unit data; outputs PCI Section layer
- Exact tool ID:
OnnxDependency_PCI_CAL
- Recommended call:
mls.pavement.onnxdependency_pci_cal(...)
- Alias:
OnnxDependency_PCI_CAL
- Required parameters: None
Parameters
| Parameter |
Type |
Required |
Default |
Description |
Parameters |
object |
No |
— |
PCI calculation parameters |
Parameters fields
| Field |
Type |
Required |
Default |
Description |
trajPath |
string |
No |
'' |
Trajectory file path (used with TRAJ source). |
rutting |
object |
No |
{'low': 0.006, 'medium': 0.013, 'high': 0.025} |
Rutting severity thresholds: { low, medium, high } in meters |
depression |
object |
No |
{'low': 0.013, 'medium': 0.025, 'high': 0.05} |
Depression severity thresholds: { low, medium, high } in meters |
potholes_diameter |
object |
No |
{'low': 0.1, 'medium': 0.2, 'high': 0.45} |
Pothole diameter severity thresholds: { low, medium, high } in meters |
potholes_depth |
object |
No |
{'low': 0.013, 'medium': 0.025, 'high': 0.05} |
Pothole depth severity thresholds: { low, medium, high } in meters |
swell |
object |
No |
{'low': 0.0061, 'medium': 0.0183, 'high': 0.0457} |
Swell severity thresholds: { low, medium, high } in meters |
bump |
object |
No |
{'low': 0.0061, 'medium': 0.0183, 'high': 0.0457} |
Bump severity thresholds: { low, medium, high } in meters |
corrugation |
object |
No |
{'low': 0.0061, 'medium': 0.0183, 'high': 0.0457} |
Corrugation severity thresholds: { low, medium, high } in meters |
shoving |
object |
No |
{'low': 0.0061, 'medium': 0.0183, 'high': 0.0457} |
Shoving severity thresholds: { low, medium, high } in meters |
Example
from gvscript import mls
params = mls.pavement.onnxdependency_pci_cal_parameters()
params.trajPath = ''
result = mls.pavement.onnxdependency_pci_cal(
Parameters=params,
)
print(result.ok, result.output, result.message)
onnxdependency_pci_report
Exports a comprehensive HTML report from the Distress and PCI Section layers
- Exact tool ID:
OnnxDependency_PCI_Report
- Recommended call:
mls.pavement.onnxdependency_pci_report(...)
- Alias:
OnnxDependency_PCI_Report
- Required parameters: None
Parameters
| Parameter |
Type |
Required |
Default |
Description |
Parameters |
object |
No |
— |
PCI report export parameters |
Parameters fields
| Field |
Type |
Required |
Default |
Description |
htmlPath |
string |
No |
'' |
Full output path for the HTML report file. If not specified, a default path is generated automatically. |
Example
from gvscript import mls
params = mls.pavement.onnxdependency_pci_report_parameters()
params.htmlPath = ''
result = mls.pavement.onnxdependency_pci_report(
Parameters=params,
)
print(result.ok, result.output, result.message)