Surface Modeling

This pipeline computes a DSM by performing pixel-level stereo matching between a left and a right image. To correct the epipolar projection if needed, this pipeline can call tie_points.

Allowed inputs

Surface modeling takes Sensor image pairs as well as (optionally) an Initial elevation as inputs, as explained in the input section. If provided, this pipeline will make use of the regularization mask of the sensor images given.

This pipeline has specific inputs when used directly : it can take a Low res DSM. It can be specified in the input field :

Table 1 Configuration

Name	Description	Type
low_res_dsm	The DSM used to reconstruct a disparity range apriori	str

If running the meta pipeline, the Low res DSM is typically set to be the previous resolution’s DSM. It makes it so locally, the disparity range to go through is very small, significantly lowering the computation time and memory used.

YAML JSON Copy

---
input:
  low_res_dsm: path/to/dsm.tif

{
    "input": {
        "low_res_dsm": "path/to/dsm.tif"
    }
}

Applications

The surface modeling pipeline uses these applications:

• grid_generation

• grid_correction

• dem_generation

• ground_truth_reprojection

• resampling

• dense_matching

• dense_match_filling

• triangulation

• point_cloud_outlier_removal

• point_cloud_rasterization

Here is a schema summarizing the inner workings of this pipeline :

Advanced Parameters

Name	Description	Type	Default value
save_intermediate_data	Save intermediate data for all applications inside this pipeline.	bool	False
keep_low_res_dir	Save intermediate data for all applications inside this pipeline.	bool	False
ground_truth_dsm	Data to be reprojected from the application ground_truth_reprojection	dict	None
geometry_plugin	Name of the geometry plugin to use and optional parameters (see geometry plugin)	str or dict	“SharelocGeometry”
land_cover_map	A global classification map used to choose the optimal configuration for dense matching	str	“global_land_cover_map.tif”
classification_to_configuration_mapping	A JSON file that maps each class of the global classification map to a corresponding Pandora configuration	str	“config_mapping.json”

Keep low res dir

The keep_low_res_dir parameter flag can be used to specify that you would like the intermediate DSMs to be saved in their respective directory, when using the meta pipeline.

By default, since keep_low_res_dir is true, you will find the intermediate DSMs in intermediate_res/out_res{resolution_value}/dsm. If save_intermediate_data was enabled for an application of an intermediate resolution, those results will be found in intermediate_res/out_res{resolution_value}/dump_dir.

The following example disables the saving of the outputs of all intermediate resolutions:

YAML JSON Copy

---
surface_modeling:
  advanced:
    all:
      keep_low_res_dir: false

{
    "surface_modeling": {
        "advanced": {
            "all": {
                "keep_low_res_dir": false
            }
        }
    }
}

Ground truth DSM

To activate the ground truth reprojection application, it is necessary to specify the required inputs in the advanced settings. For this, a dictionary named ground_truth_dsm must be added, containing the keys presented in the following table. By default, the used dsm is considered on ellipsoid. If not, fill the geoid parameter.

Name	Description	Type	Available value	Default value	Required
dsm	Path to ground truth dsm (Lidar for example)	string			Yes
geoid	DSM geoid.	bool or string		False	No
auxiliary_data	The lidar auxiliaries data	dict		None	No
auxiliary_data_interpolation	The lidar auxiliaries data interpolator	dict		None (nearest if auxiliary_data is not None)	No

Note

The parameter geoid refers to the vertical reference of the ground truth DSM. It can be set as a string to provide the path to a geoid file on disk, or as a boolean: if set to True CARS default geoid is used, if set to False no vertical offset is applied (ellipsoid reference).

Example:

YAML JSON Copy

surface_modeling:
  advanced:
    1:
      ground_truth_dsm:
        dsm: path/to/ground/truth/dsm.tif
        auxiliary_data:
          classification: path/to/classification.tif
          texture: path/to/image.tif
        auxiliary_data_interpolation:
          classification: nearest
          texture: linear

{
    "surface_modeling": {
        "advanced": {
            "1": {
                "ground_truth_dsm": {
                    "dsm": "path/to/ground/truth/dsm.tif",
                    "auxiliary_data": {
                        "classification": "path/to/classification.tif",
                        "texture": "path/to/image.tif"
                    },
                    "auxiliary_data_interpolation": {
                        "classification": "nearest",
                        "texture": "linear"
                    }
                }
            }
        }
    }
}