Advanced configuration

This section describes CARS main advanced configuration structure through a json configuration file.

The structure follows this organization:

{
    "inputs": {},
    "orchestrator": {},
    "applications": {},
    "output": {},
    "advanced": {}
}

Warning

Be careful with commas to separate each section. None needed for the last json element.

This key is optional and allows to redefine parameters for each application used in pipeline.

This section describes all possible configuration of CARS applications.

CARS applications are defined and called by their name in applications configuration section:

"applications":{
    "application_name": {
        "method": "application_dependent",
        "parameter1": 3,
        "parameter2": 0.3
    }
}

Be careful with these parameters: no mechanism ensures consistency between applications for now. And some parameters can degrade performance and DSM quality heavily. The default parameters have been set as a robust and consistent end to end configuration for the whole pipeline.

Name: “grid_generation”

Description

From sensors image, compute the stereo-rectification grids

Configuration

Name	Description	Type	Available values	Default value	Required
method	Method for grid generation	string	“epipolar”	epipolar	No
epi_step	Step of the deformation grid in nb. of pixels	int	should be > 0	30	No
save_intermediate_data	Save the generated grids	boolean		false	No

Example

"applications": {
    "grid_generation": {
        "method": "epipolar",
        "epi_step": 35
    }
},

Name: “resampling”

Description

Input images are resampled with grids.

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for resampling	string	“bicubic”	“bicubic”	No
strip_height	Height of strip (only when tiling is done by strip)	int	should be > 0	60	No
step	Horizontal step for resampling inside a strip	int	should be > 0	500	No
save_intermediate_data	Save epipolar images and color	boolean		false	No

Example

"applications": {
    "resampling": {
        "method": "bicubic",
        "epi_tile_size": 600
    }
},

Name: “sparse_matching”

Description

Compute keypoints matches on pair images

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for sparse matching	string	“sift”, “pandora”	“sift”	No
disparity_margin	Add a margin to min and max disparity as percent of the disparity range.	float		0.02	No
epipolar_error_upper_bound	Expected upper bound for epipolar error in pixels	float	should be > 0	10.0	No
epipolar_error_maximum_bias	Maximum bias for epipolar error in pixels	float	should be >= 0	0.0	No
sift_back_matching	Also check that right vs. left gives same match	boolean		true	No
matches_filter_knn	Number of neighbors used to measure isolation of matches and detect isolated matches	int	should be > 0	25	No
matches_filter_dev_factor	Factor of deviation of isolation of matches to compute threshold of outliers	int, float	should be > 0	3.0	No
save_intermediate_data	Save matches in epipolar geometry (4 first columns) and sensor geometry (4 last columns)	boolean		false	No
strip_margin	Margin to use on strip	int	should be > 0	10	No
elevation_delta_lower_bound	Expected lower bound for elevation delta with respect to input low resolution dem in meters	int, float		None	No
elevation_delta_upper_bound	Expected upper bound for elevation delta with respect to input low resolution dem in meters	int, float		None	No
minimum_nb_matches	Minimum number of matches that must be computed to continue pipeline	int	should be > 0	100	No

Note

‘elevation_delta_lower_bound’ and ‘elevation_delta_upper_bound’ are overidden to [-1000, 9000] in default pipeline if no initial elevation is set. If initial elevation is set, it is overridden to [-500, 1000].

Sift:

Name	Description	Type	Available value	Default value	Required
disparity_outliers_rejection_percent	Percentage of outliers to reject	float	between 0 and 1	0.1	No
sift_matching_threshold	Threshold for the ratio to nearest second match	float	should be > 0	0.7	No
sift_n_octave	The number of octaves of the Difference of Gaussians scale space	int	should be > 0	8	No
sift_n_scale_per_octave	The numbers of levels per octave of the Difference of Gaussians scale space	int	should be > 0	3	No
sift_peak_threshold	Constrast threshold to discard a match (at None it will be set according to image type)	float	should be > 0	4.0	No
sift_edge_threshold	Distance to image edge threshold to discard a match	float		10.0	No
sift_magnification	The descriptor magnification factor	float	should be > 0	7.0	No
sift_window_size	smaller values let the center of the descriptor count more	int	should be > 0	2	No
decimation_factor	Reduce the number of sifts	int	should be > 0	30	No

For more information about these parameters, please refer to the VLFEAT SIFT documentation.

Note

For the decimation factor, a value of 33 means that we divide the number of sift by 3, a value of 100 means that we do not decimate them

Pandora:

Name	Description	Type	Available value	Default value	Required
resolution	Resolution at which the image will be downsampled for the use of pandora	int, list	should be > 0	4	No
loader_conf	Pandora configuration that will be used	dict		Pandora default conf	No
connection_val	distance to use to consider that two points are connected	float	should be > 0	3.0	No
nb_pts_threshold	number of points to use to identify small clusters to filter	int	should be > 0	80	No
filtered_elt_pos	if filtered_elt_pos is set to True, the removed points positions in their original epipolar images are returned, otherwise it is set to None	bool		False	No
clusters_distance_threshold	distance to use to consider if two points clusters are far from each other or not	float		None	No

Warning

There is a particular case with the sparse_matching application because it can be called twice. So you can configure the application twice , once for the sift, the other for pandora method. Because it is not possible to define twice the application_name on your json configuration file, we have decided to configure those two applications with :

sparse_matching.sift sparse_matching.pandora

Each one is associated to a particular sparse_matching method* Therefore, is it not possible to use the key sparse_matching and to select the method.

Example

"applications": {
    "sparse_matching.sift": {
        "method": "sift",
        "disparity_margin": 0.01
    },
    "sparse_matching.pandora":{
        "method": "pandora",
        "resolution": [4, 2]
    }
},

Name: “dem_generation”

Description

Generates dem from sparse matches.

3 dems are generated, with different methods: * median * min * max

The DEMs are generated in the application dump directory

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for dem_generation	string	“dichotomic”	“dichotomic”	Yes
resolution	Resolution of dem, in meter	int, float	should be > 0	90	No
margin	Margin to use on the border of dem, in meter	int, float	should be > 0	6000	No
percentile	Percentile of matches to ignore in min and max functions	int	should be > 0	1	No
min_number_matches	Minimum number of matches needed to have a valid tile	int	should be > 0	30	No
height_margin	Height margin [margin min, margin max], in meter	int		20	No
fillnodata_max_search_distance	Max search distance for rasterio fill nodata	int	should be > 0	3	No
min_dem	Min value that has to be reached by dem_min	int	should be < 0	-500	No
max_dem	Max value that has to be reached by dem_max	int	should be > 0	1000	No
save_intermediate_data	Save DEM as TIF	boolean		false	No

Example

"applications": {
    "dem_generation": {
        "method": "dichotomic",
        "min_number_matches": 20
    }
}

Name: “ground_truth_reprojection”

Description

Generates epipolar and sensor ground truth from input dsm using direct localization. * Sensor ground truth contains altitude in sensor geometry. * Epipolar ground truth contains disparity map in epipolar geometry.

Name	Description	Type	Available value	Default value	Required
method	Method for ground_truth_reprojection	string	“direct_loc”		Yes
target	Type of ground truth	string	“epipolar”, “sensor”, “all”	“epipolar”	No
tile_size	Tile size to use	int		2500	No

Example

"applications": {
    "ground_truth_reprojection": {
        "method": "direct_loc",
        "target": "all"
    }
}

Name: “dense_matching”

Description

Compute the disparity map from stereo-rectified pair images

Table 3 Configuration
Name	Description	Type	Available value	Default value	Required
method	Method for dense matching	string	“census_sgm”, “mccnn_sgm”	“census_sgm”	No
loader	external library use to compute dense matching	string	“pandora”	“pandora”	No
loader_conf	Configuration associated with loader, dictionary or path to config	dict or str			No
min_elevation_offset	Override minimum disparity from prepare step with this offset in meters	int		None	No
max_elevation_offset	Override maximum disparity from prepare step with this offset in meters	int	should be > min	None	No
disp_min_threshold	Override minimum disparity when less than lower bound	int		None	No
disp_max_threshold	Override maximum disparity when greater than upper bound	int	should be > min	None	No
min_epi_tile_size	Lower bound of optimal epipolar tile size for dense matching	int	should be > 0	300	No
max_epi_tile_size	Upper bound of optimal epipolar tile size for dense matching	int	should be > 0 and > min	1500	No
epipolar_tile_margin_in_percent	Size of the margin used for dense matching (percent of tile size)	int		60	No
performance_map_method	Compute performance map with selected method(s).	str, list, None	“risk”, “intervals”	“risk”	No
perf_eta_max_ambiguity	Ambiguity confidence eta max used for performance map (risk method)	float		0.99	No
perf_eta_max_risk	Risk confidence eta max used for performance map (risk method)	float		0.25	No
perf_eta_step	Risk and Ambiguity confidence eta step used for performance map (risk method)	float		0.04	No
perf_ambiguity_threshold	Maximal ambiguity considered for performance map (risk method)	float		0.6	No
save_intermediate_data	Save disparity map and disparity confidence	boolean		false	No
use_global_disp_range	If true, use global disparity range, otherwise local range estimation	boolean		false	No
local_disp_grid_step	Step of disparity min/ max grid used to resample dense disparity range	int		30	No
disp_range_propagation_filter_size	Filter size of local min/max disparity, to propagate local min/max	int	should be > 0	300	No
use_cross_validation	Add cross validation step	bool		false	No

See Pandora documentation for more information.

Example

"applications": {
    "dense_matching": {
        "method": "census_sgm",
        "loader": "pandora",
        "loader_conf": "path_to_user_pandora_configuration"
    }
},

Note

Disparity range can be global (same disparity range used for each tile), or local (disparity range is estimated for each tile with dem min/max).
When user activate the generation of performance map, this map transits until being rasterized. Performance map is managed as a confidence map.
To save the confidence, the save_intermediate_data parameter should be activated.

Name: “dense_match_filling”

Description

Fill holes in dense matches map. This uses the holes detected with the HoleDetection application. The holes correspond to the area masked for dense matching.

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for hole detection	string	“plane”, “zero_padding”	“plane”	No
save_intermediate_data	Save disparity map	boolean		False	No

Method plane:

Name	Description	Type	Available value	Default value	Required
classification	Classification band name	List[str]		None	No
interpolation_type	Interpolation type	string	“pandora”	“pandora”	No
interpolation_method	Method for hole interpolation	string	“mc_cnn”	“mc_cnn”	No
max_search_distance	Maximum search distance	int		100	No
smoothing_iterations	Number of smoothing iterations	int		1	No
ignore_nodata_at_disp_mask_borders	Ignore nodata at borders	boolean		false	No
ignore_zero_fill_disp_mask_values	Ignore zeros	boolean		true	No
ignore_extrema_disp_values	Ignore extrema values	boolean		true	No
nb_pix	Margin used for mask	int		20	No
percent_to_erode	Percentage to erode	float		0.2	No

Method zero_padding:

The zero_padding method fills the disparity with zeros where the selected classification values are non-zero values.

Name	Description	Type	Available value	Default value	Required
classification	Classification band name	List[str]		None	No

Note

The classification of second input is not given. Only the first disparity will be filled with zero value.
The filled area will be considered as a valid disparity mask.

Warning

There is a particular case with the dense_match_filling application because it is called twice. The eighth step consists of fill dense matches via two consecutive methods. So you can configure the application twice , once for the plane, the other for zero_padding method. Because it is not possible to define twice the application_name on your json configuration file, we have decided to configure those two applications with :

dense_match_filling.1
dense_match_filling.2

Each one is associated to a particular dense_match_filling method* Therefore, is it not possible to use the key dense_match_filling and to select the method.

Example

"applications": {
    "dense_match_filling.1": {
        "method": "plane",
        "classification": ["water"],
        "save_intermediate_data": true
    },
    "dense_match_filling.2": {
        "method": "zero_padding",
        "classification": ["cloud", "snow"],
        "save_intermediate_data": true
    }
},

Name: “triangulation”

Description

Triangulating the sights and get for each point of the reference image a latitude, longitude, altitude point

Configuration

Name	Description	Type	Available values \| Default value		Required
method	Method for triangulation	string	“line_of_sight_intersection”	“line_of_sight_intersection”	No
snap_to_img1	If all pairs share the same left image, modify lines of sight of secondary images to cross those of the ref image	boolean		false	No
save_intermediate_data	Save depth map as TIF, LAZ and CSV	boolean		false	No

Example

"applications": {
    "triangulation": {
        "method": "line_of_sight_intersection",
        "snap_to_img1": true
    }
},

Name: “point_cloud_fusion”

Description

Merge points clouds coming from each pair

Only one method is available for now: “mapping_to_terrain_tiles”

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for fusion	string	“mapping_to_terrain_tiles”	“mapping_to_terrain_tiles”	No
save_intermediate_data	Save points clouds as laz and csv format	boolean		false	No
save_by_pair	Enable points cloud saving by pair	boolean		false	No

Example

"applications": {
    "point_cloud_fusion": {
        "method": "mapping_to_terrain_tiles",
        "save_intermediate_data": true,
        "save_by_pair": true,
    }
},

Note

When save_intermediate_data is activated, multiple Laz and csv files are saved, corresponding to each processed terrain tiles. Please, see the section Merge Laz files to merge them into one single file. save_by_pair parameter enables saving by input pair. The csv/laz name aggregates row, col and corresponding pair key.

Name: “point_cloud_outlier_removal”

Description

Point cloud outlier removal

Configuration

Name	Description	Type	Available value	Default value	Required
method	Method for point cloud outlier removal	string	“statistical”, “small_components”	“statistical”	No
save_intermediate_data	Save points clouds as laz and csv format	boolean		false	No

If method is statistical:

Name	Type	Available value	Default value	Required
activated	boolean		True	No
k	int	should be > 0	50	No
std_dev_factor	float	should be > 0	5.0	No
use_median	bool		True	No
half_epipolar_size	int		5	No

If method is small_components

Name	Type	Default value	Required
activated	boolean	True	No
on_ground_margin	int	10	No
connection_distance	float	3.0	No
nb_points_threshold	int	50	No
clusters_distance_threshold	float	None	No
half_epipolar_size	int	5	No

Warning

There is a particular case with the Point Cloud outlier removal application because it is called twice. The ninth step consists of Filter the 3D points cloud via two consecutive filters. So you can configure the application twice , once for the small component filters, the other for statistical filter. Because it is not possible to define twice the application_name on your json configuration file, we have decided to configure those two applications with :

point_cloud_outlier_removal.1
point_cloud_outlier_removal.2

Each one is associated to a particular point_cloud_outlier_removal method* Therefore, is it not possible to use the key point_cloud_outlier_removal and to select the method.

Example

"applications": {
    "point_cloud_outlier_removal.1": {
        "method": "small_components",
        "on_ground_margin": 10,
        "save_intermediate_data": true,
    },
    "point_cloud_outlier_removal.2": {
        "method": "statistical",
        "k": 10,
        "save_intermediate_data": true,
    }
}

Name: “point_cloud_rasterization”

Description

Project altitudes on regular grid.

Only one simple gaussian method is available for now.

Table 4 Configuration
Name	Description	Type	Available value	Default value	Required
method		string	“simple_gaussian”	simple_gaussian	No
dsm_radius		float, int		1.0	No
sigma		float		None	No
grid_points_division_factor		int		None	No
dsm_no_data		int		-32768
color_no_data		int		0
color_dtype	By default, it’s retrieved from the input color Otherwise, specify an image type	string	“uint8”, “uint16” “float32” …	None	No
msk_no_data	No data value for mask and classif	int		255
save_intermediate_data	Save all layers from input point cloud in application dump_dir	boolean		false	No

Example

"applications": {
    "point_cloud_rasterization": {
        "method": "simple_gaussian",
        "dsm_radius": 1.5
    }
},

Name: “dsm_filling”

Description

Fill in the missing values of the DSM by using the DEM’s elevation. This application replaces the existing dsm.tif.

Only one method is available for now: “bulldozer”.

Note

When save_intermediate_data is activated, the folder dump_dir/dsm_filling will contain :

The replaced dsm.tif, saved under dump_dir/dsm_filling/dsm_not_filled.tif
The dsm given to Bulldozer as input, saved under dump_dir/dsm_filling/dsm_filled_with_dem_not_smoothed.tif
The configuration given to Bulldozer, saved under dump_dir/dsm_filling/bulldozer_config.yaml
All the outputs generated by Bulldozer, saved under dump_dir/dsm_filling/bulldozer/

Configuration

Name	Description	Type	Available values	Default value	Required
method	Method for filling	string	“bulldozer”	“bulldozer”	No
activated	Activates the filling	boolean		false	No
save_intermediate_data	Saves the temporary data in dump_dir	boolean		false	No

Example

"applications": {
    "dsm_filling": {
        "method": "bulldozer",
        "activated": true,
    }
},

Name: “auxiliary_filling”

Description

Fill in the missing values of the color and classification by using information from sensor inputs This application replaces the existing color.tif and classification.tif.

The application retrieves color and classification information by performing inverse location on the input sensor images. It is therefore necessary to provide the sensors category in inputs configuration in order to use this application, even when depth_map are provided as input. The pairing information is also required: when searching for color information, the application will always look in the first sensor of the pair and then in the second, if no information for the given pixel is found in the first sensor. The final filled value of the pixel is the average of the contribution of each pair. The classification information is a logical OR of all classifications.

In fill_nan mode, only the pixels that are no-data in the auxiliary images that are valid in the reference dsm will be filled while in full mode all valid pixel from the reference dsm are filled.

If use_mask is set to true, the color data from a sensor will not be used if the corresponding sensor mask value is false. If the pixel is masked in all images, the filled color will be the average of the first sensor color of each pair

When save_intermediate_data is activated, the folder dump_dir/auxiliary_filling will contain the non-filled color and classification.

Configuration

Name	Description	Type	Available values	Default value	Required
method	Method for filling	string	“auxiliary_filling_from_sensors”	“auxiliary_filling_from_sensors”	No
activated	Activates the filling	boolean		false	No
mode	Processing mode	string	“fill_nan”, “full”	false	No
use_mask	Use mask information from input sensors	boolean		true	No
color_interpolator	interpolator used for color interpolation	string	“linear”, “nearest”, “cubic”	“linear”	No
save_intermediate_data	Saves the temporary data in dump_dir	boolean		false	No

Here are the advanced parameters. This key is optionnal and can be useful if you want to use CARS more as a developer.

Table 5 Configuration
Name	Description	Type	Default value	Required
save_intermediate data	Save intermediate data for all applications	bool	False	Yes
use_epipolar_a_priori	Active epipolar a priori	bool	False	Yes
epipolar_a_priori	Provide epipolar a priori information (see section below)	dict		No
terrain_a_priori	Provide terrain a priori information (see section below)	dict		No
debug_with_roi	Use input ROI with the tiling of the entire image (see Inputs section)	bool	False	No
merging	Merge point clouds before rasterization (soon to be deprecated)	bool	False	No
performance_map_classes	List defining interval: [a,b,c,d] generates [[a,b],[b,c],[c,d]] intervals used in the performance map classification. If null, raw performance map is given	list or None	[0, 0.968, 1.13375, 1.295, 1.604, 2.423, 3.428]	No
ground_truth_dsm	Datas to be reprojected from the application ground_truth_reprojection	dict		No
phasing	Phase to use for DSM {“point” : (x,y) , “epsg”: epsg}	dict		No
geometry_plugin	Name of the geometry plugin to use and optional parameters	str or dict	“SharelocGeometry”	No
pipeline	Name of the pipeline to use	str	“default”	No

The save_intermediate_data flag can be used to activate and deactivate the saving of the possible output of applications.

It is set in the advanced category and can be overloaded in each application separately. It default to false, meaning that no intermediate product in saved). Intermediate data are saved in the dump_dir folder found in CARS output directory, with a subfolder corresponding to each application.

For exemple setting save_intermediate_data to true in advanced and to false in application/point_cloud_rasterization will activate product saving in all applications excepting point_cloud_rasterization. Conversely, setting it to false in advanced and to true in application/point_cloud_rasterization will only save rasterization outputs.

Intermediate data refers to all files that are not part of an output product. Files that compose an output product will not be found in the application dump directory. For exemple if dsm is requested as output product, the dsm.tif files and all activated dsm auxiliary files will not be found in rasterization dump directory. This directory will still contain the files generated by the rasterization application that are not part of the dsm product.

"advanced": {
    "save_intermediate_data": true
    }

The CARS pipeline produces a used_conf.json in the outdir that contains the epipolar_a_priori information for each sensor image pairs. If you wish to re-run CARS, this time by skipping the sparse matching, you can use the used_conf.json as the new input configuration, with its use_epipolar_a_priori parameter set to True.

For each sensor images, the epipolar a priori are filled as following:

Name	Description	Type	Default value	Required
grid_correction	The grid correction coefficients	list		if use_epipolar_a_priori is True
disparity_range	The disparity range [disp_min, disp_max]	list		if use_epipolar_a_priori is True

Note

The grid correction coefficients are based on bilinear model with 6 parameters [x1,x2,x3,y1,y2,y3]. The None value produces no grid correction (equivalent to parameters [0,0,0,0,0,0]).

The terrain_a_priori is used at the same time that epipolar_a_priori. If use_epipolar_a_priori is activated, epipolar_a_priori and terrain_a_priori must be provided. The terrain_a_priori data dict is produced during low or full resolution dsm pipeline.

The terrain a priori is initially populated with DEM information.

Name	Description	Type	Required
dem_median	DEM generated with median function	str	if use_epipolar_a_priori is True
dem_min	DEM generated with min function	str	if use_epipolar_a_priori is True
dem_max	DEM generated with max function	str	if use_epipolar_a_priori is True

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:

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

Phase can be added to make sure multiple DSMs can be merged in “dsm -> dsm” pipeline. “point” and “epsg” of point must be specified

Name	Description	Type	Default value	Available values	Required
point	Point to phase on	tuple	None		False
epsg	Epsg of point	int	None		False

"phasing": {
    "point": [32000, 30000],
    "epsg": 32530
}

This section describes configuration of the geometry plugins for CARS, please refer to Plugins section for details on plugins installation.

Name	Description	Type	Default value	Available values	Required
geometry_plugin	The plugin to use	str or dict	“SharelocGeometry”	“SharelocGeometry”	False

geometry_plugin allow user to specify other parameters, through a dictionary:

Name	Description	Type	Default value	Available values	Required
plugin_name	The plugin name to use	str	“SharelocGeometry”	“SharelocGeometry”	False
interpolator	Interpolator to use	str	“cubic”	“cubic” , “linear”	False

To use Shareloc geometry library, CARS input configuration should be defined as :

{
    "inputs": {
    "sensors": {
      "one": {
        "image": "img1.tif",
        "geomodel": {
          "path": "img1.geom",
          "model_type": "RPC"
        },
      },
      "two": {
        "image": "img2.tif",
        "geomodel": {
          "path": "img2.geom",
          "model_type": "RPC"
        },
      }
    },
    "pairing": [["one", "two"]],
    "initial_elevation": {
        "dem": "path/to/srtm_file.tif"
      },
    },
    "advanced":{
        "geometry_plugin": "SharelocGeometry"
    }
}

geometry_plugin specify the plugin to use, but other configuration parameters can be specified :

"advanced":{
    "geometry_plugin": {
        "plugin_name": "SharelocGeometry",
        "interpolator": "cubic"
    }
}

The particularities in the configuration file are:

geomodel.model_type: Depending on the nature of the geometric models indicated above, this field as to be defined as RPC or GRID. By default, “RPC”.
initial_elevation: Field contains the path to the file corresponding the srtm tiles covering the production (and not a directory !!)
geometry_plugin: Parameter configured to “SharelocGeometry” to use Shareloc plugin.

Parameter can also be defined as a string path instead of a dictionary in the configuration. In this case, geomodel parameter will be changed to a dictionary before launching the pipeline. The dictionary will be :

{
  "path": "img1.geom",
  "model_type": "RPC"
}

Note

Be aware that geometric models must therefore be opened by Shareloc directly in this case, and supported sensors may evolve.

The pipeline key is optional and allows users to choose the pipeline they would like to run. By default, CARS has a single pipeline: default. This pipeline is modular and can be adapted to your needs. This sections provides examples of specific configurations.

Installed plugins may provide additional pipelines. The inputs and outputs are specific to each pipeline. This section describes the pipeline available in CARS.

Name	Description	Type	Default value	Available values	Required
pipeline	The pipeline to use	str	“default”	“default”	False

"advanced": {
    "pipeline": "your_pipeline_name"
    }
}