The orthomapping python API allows automating orthomapping tasks in the server environment.
For more information about orthomapping workflows in ArcGIS, please visit the help documentation at Block adjustment for mosaic datasets
is_supported
- arcgis.raster.orthomapping.is_supported(gis=None)
Returns True if the GIS supports orthomapping. If a gis isn’t specified, checks if
active_gis()supports raster analytics
compute_sensor_model
- arcgis.raster.orthomapping.compute_sensor_model(image_collection, mode='Quick', location_accuracy='High', context=None, *, gis=None, future=False, **kwargs)
compute_sensor_model computes the bundle block adjustment for the image collection and applies the frame xform to the images. It will also generate the control point table, solution table, solution points table and flight path table. These tables will not be published as Portal items.
Parameter
Description
image_collection
Required, the input image collection on which to compute the sensor model. The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
mode
Optional string. the mode to be used for bundle block adjustment Only the following modes are supported:
‘Quick’ : Computes tie points and adjustment at 8x of the source imagery resolution
‘Full’ : adjust the images in Quick mode then at 1x of the source imagery resolution
‘Refine’ : adjust the image at 1x of the source imagery resolution
By default, ‘Quick’ mode is applied to compute the sensor model.
location_accuracy
Optional string. this option allows users to specify the GPS location accuracy level of the source image. It determines how far the underline tool will search for neighboring matching images, then calculate tie points and compute adjustments.
Possible values for location_accuracy are:
‘VeryHigh’ : Imagery was collected with a high-accuracy, differential GPS, such as RTK or PPK. This option will hold image locations fixed during block adjustment
‘High’ : GPS accuracy is 0 to 10 meters, and the tool uses a maximum of 4 by 3 images
‘Medium’ : GPS accuracy of 10 to 20 meters, and the tool uses a maximum of 4 by 6 images
‘Low’ : GPS accuracy of 20 to 50 meters, and the tool uses a maximum of 4 by 12 images
‘VeryLow’ : GPS accuracy is more than 50 meters, and the tool uses a maximum of 4 by 20 images
The default location_accuracy is ‘High’
context
Optional dictionary. The context parameter is used to configure additional client settings for block adjustment. The supported configurable parameters are for compute mosaic dataset candidates after the adjustment.
The possible keys for the context dictionary are:
parallelProcessingFactor : The specified number or percentage of processes will be used for the analysis. The default value is “50%”.
computeCandidate : Indicates whether Compute Mosaic Candidates will run inside the service task. Default value is False.
maxOverlap : Specifies the maximum area overlap for running the Compute Mosaic Candidates tool inside the task. The default value is 0.6 .
maxLoss : Specifies the maximum area loss allowed for running the Compute Mosaic Candidates tool inside the task. The default value is 0.05 .
initPointResolution : Specifies the initial tie point resolution for running the Compute Camera Model tool inside the task. The default value is 8.0 .
maxResidual : Specifies the maximum residual for running the Compute Block Adjustment and Compute Camera Model tools inside the task. The default value is 5.0 .
adjustOptions : Specifies the adjustment options for running the Compute Block Adjustment tool inside the task. The default value is empty.
pointSimilarity : Specifies the similarity for running the Compute Tie Points tool inside the task. The default value is MEDIUM.
pointDensity : Specifies the point density for running the Compute Tie Points tool inside the task. The default value is MEDIUM.
pointDistribution : Specifies the point distribution for running the Compute Tie Points tool inside the task. The default value is RANDOM.
polygonMask : Specifies the input mask for running the Compute Tie Points tool inside the task. Default value is empty.
regenTiepoints : Indicates whether Compute Tie Points will rerun inside the service task if tie points feature class exists. The default value is True.
Example:
{ “computeCandidate”: False, “maxOverlap”: 0.6, “maxLoss”: 0.05, }
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The imagery layer url
alter_processing_states
- arcgis.raster.orthomapping.alter_processing_states(image_collection, new_states, *, gis=None, future=False, **kwargs)
Alter the processing states of the image collection. The states are stored as key property “Orthomapping”. The content of the state is a dictionary including several properties which can be set based on the process done on the image collection.
Parameter
Description
image_collection
Required, This is the image collection that will be adjusted.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
new_states
Required dictionary. The state to set on the image_collection
This a dictionary of states that should be set on the image collection The new states that can be set on the image collection are: blockadjustment, dem, gcp, seamlines, colorcorrection, adjust_index, imagetype
Example:
{“blockadjustment”: “raw”,“dem”: “Dense_Natual_Neighbor”,“seamlines”:”VORONOI”,“colorcorrection”:”SingleColor”,“imagetype”: “UAV/UAS”,“adjust_index”: 0}gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The result will be the newly set states dictionary
get_processing_states
- arcgis.raster.orthomapping.get_processing_states(image_collection, *, gis=None, future=False, **kwargs)
Retrieve the processing states of the image collection
Parameter
Description
image_collection
Required, This is the image collection that will be adjusted.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The result will be the newly set states dictionary
match_control_points
- arcgis.raster.orthomapping.match_control_points(image_collection, control_points, similarity='High', context=None, *, gis=None, future=False, **kwargs)
The match_control_points is a function that takes a collection of ground control points as input (control points to be specified as a list of dictionary objects), and each of the ground control points needs at least one matching tie point in the control point sets. The function will compute the remaining matching tie points for all control point sets.
Parameter
Description
image_collection
Required, the input image collection that will be adjusted.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
control_points
Required, a list of control point sets objects.
The schema of control points follows the schema of the mosaic dataset control point table.
The control point object should contain the point geometry, pointID, type, status and the imagePoints. (the imagePoints attribute inside the control points object lists the imageIDs)
– pointID (int) - The ID of the point within the control point table.
– type (int) - The type of the control point as determined by its numeric value
1: Tie Point 2: Ground Control Point. 3: Check Point
– status (int) - The status of the point. A value of 0 indicates that the point will not be used in computation. A non-zero value indicates otherwise.
– imageID (int) - Image identification using the ObjectID from the mosaic dataset footprint table.
Example:
[{“status”: 1,“type”: 2,“x”: -117.0926538,“y”: 34.00704253,“z”: 634.2175,“spatialReference”: {“wkid”: 4326}, // default WGS84“imagePointSpatialReference”: {}, // default ICS“pointId”: 1,“xyAccuracy”: “0.008602325”,“zAccuracy”: “0.015”,“imagePoints”: [{“imageID”: 1,“x”: 2986.5435987557084,“y”: -2042.5193648409431,“u”: 3057.4580682832734,“v”: -1909.1506872159698},{“imageID”: 2,“x”: 1838.2814361401108,“y”: -2594.5280063817972,“u”: 3059.4079724863363,“v”: -2961.292545463305},{“imageID”: 12,“x”: 5332.855578204663,“y”: -2533.2805429751907,“u”: 614.2338676573158,“v”: -165.10836768947297},{“imageID”: 13,“x”: 4932.0895715254455,“y”: -1833.8401744114287,“u”: 616.9396928182223,“v”: -1243.1445126959693}]},……]similarity
Optional string. Choose the tolerance level for your control point matching.
Low- The similarity tolerance for finding control points will be low. This option will produce the most control points, but some may have a higher level of error.
Medium - The similarity tolerance for finding control points will be medium.
High - The similarity tolerance for finding control points will be high. This option will produce the least number of control points, but each matching pair will have a lower level of error. This is the default.
context
Optional dictionary.Additional settings such as the input control points spatial reference can be specified here.
For Example:
{“groundControlPointsSpatialReference”: {“wkid”: 3459}, “imagePointSpatialReference”: {“wkid”: 3459}}
Note: The ground control points spatial reference and image point spatial reference spatial reference set in the context parameter is to decide the returned point set’s ground control points spatial reference and image point spatial reference. If these two parameters are not set here, the tool will use the spatial reference defined in the input point set. And if no spatial reference is defined in the point set, then the default ground control points coordinates are in lon/lat and image points coordinates are in image coordinate system.
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
A list of dictionary objects
color_correction
- arcgis.raster.orthomapping.color_correction(image_collection, color_correction_method, dodging_surface_type, target_image=None, context=None, *, gis=None, future=False, **kwargs)
Color balance the image collection. Refer to the Color Balance Mosaic Dataset GP tool for documentation on color balancing mosaic datasets.
Parameter
Description
image_collection
Required. This is the image collection that will be adjusted.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
color_correction_method
Required string. This is the method that will be used for color correction computation. The available options are:
Dodging-Change each pixel’s value toward a target color. With this technique, you must also choose the type of target color surface, which affects the target color. Dodging tends to give the best result in most cases.
Histogram-Change each pixel’s value according to its relationship with a target histogram. The target histogram can be derived from all of the rasters, or you can specify a raster. This technique works well when all of the rasters have a similar histogram.
Standard_Deviation-Change each of the pixel’s values according to its relationship with the histogram of the target raster, within one standard deviation. The standard deviation can be calculated from all of the rasters in the mosaic dataset, or you can specify a target raster. This technique works best when all of the rasters have normal distributions.
dodging_surface_type
Required string.When using the Dodging balance method, each pixel needs a target color, which is determined by the surface type.
Single_Color-Use when there are only a small number of raster datasets and a few different types of ground objects. If there are too many raster datasets or too many types of ground surfaces, the output color may become blurred. All the pixels are altered toward a single color point-the average of all pixels.
Color_Grid- Use when you have a large number of raster datasets, or areas with a large number of diverse ground objects. Pixels are altered toward multiple target colors, which are distributed across the mosaic dataset.
First_Order- This technique tends to create a smoother color change and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All pixels are altered toward many points obtained from the two-dimensional polynomial slanted plane.
Second_Order-This technique tends to create a smoother color change and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All input pixels are altered toward a set of multiple points obtained from the two-dimensional polynomial parabolic surface.
Third_Order-This technique tends to create a smoother color change and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All input pixels are altered toward multiple points obtained from the cubic surface.
target_image
Optional. The image service you want to use to color balance the images in the image collection. It can be a portal Item or an image service URL or a URI
context
Optional dictionary. It contains additional settings that allows users to customize the statistics computation settings.
Example:
{“skipRows”: 10, “skipCols”: 10, “reCalculateStats”: “OVERWRITE”}
gis
Optional
GIS. the GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The imagery layer url
compute_control_points
- arcgis.raster.orthomapping.compute_control_points(image_collection, reference_image=None, image_location_accuracy='High', context=None, *, gis=None, future=False, **kwargs)
This service tool is used for computing matching control points between images within an image collection and/or matching control points between the image collection images and the reference image. Compute Control Points
Parameter
Description
image_collection
Required. This is the image collection that will be adjusted.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
reference_image
This is the reference image service that can be used to generate ground control points set with the image service. It can be a portal Item or an image service URL or a URI
image_location_accuracy
Optional string. This option allows you to specify the GPS location accuracy level of the source image. It determines how far the tool will search for neighboring matching images for calculating tie points and block adjustments.
The following are the available options: Low, Medium, High
Low- GPS accuracy of 20 to 50 meters, and the tool uses a maximum of 4 by 12 images.
Medium- GPS accuracy of 10 to 20 meters, and the tool uses a maximum of 4 by 6 images.
High- GPS accuracy of 0 to 10 meters, and the tool uses a maximum of 4 by 3 images.
If the image collection is created from satellite data, it will be automatically switched to use RPC adjustment mode. In this case, the mode need not be explicitly set by the user.
Default is High
context
Optional dictionary. Context contains additional environment settings that affect output control points generation.
Possible keys and their possible values are:
pointSimilarity- Sets LOW, MEDIUM, or HIGH tolerance for computing control points with varying levels of potential error.
LOW tolerance will produce the most control point, but may have a higher level of error.
HIGH tolerance will produce the least number of control point, but each matching pair will have a lower level of error.
MEDIUM tolerance will set the similarity tolerance to medium.
pointDensity- Sets the number of tie points (LOW, MEDIUM, or HIGH), to be created.
LOW point density will create the fewest number of tie points.
MEDIUM point density will create a moderate number of tie points.
HIGH point density will create the highest number of tie points.
pointDistribution- Randomly generates points that are better for overlapping areas with irregular shapes.
RANDOM- will generate points that are better for overlapping areas with irregular shapes.
REGULAR- will generate points based on a fixed pattern and uses the point density to determine how frequently to create points.
Example:
{ “pointSimilarity”:”MEDIUM”, “pointDensity”: “MEDIUM”, “pointDistribution”: “RANDOM” }
gis
Optional
GIS. the GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The imagery layer url
compute_seamlines
- arcgis.raster.orthomapping.compute_seamlines(image_collection, seamlines_method, context=None, *, gis=None, future=False, **kwargs)
Compute seamlines on the image collection. This service tool is used to compute seamlines for the image collection, usually after the image collection has been block adjusted. Seamlines are helpful for generating the seamless mosaicked display of overlapped images in image collection. The seamlines are computed only for candidates that will eventually be used for generating the result ortho-mosaicked image.
Parameter
Description
image_collection
Required, the input image collection that will be adjusted. The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
seamlines_method
Required string. These are supported methods for generated seamlines for the image collection.
VORONOI-Generate seamlines using the area Voronoi diagram.
DISPARITY-Generate seamlines based on the disparity images of stereo pairs.
GEOMETRY - Generate seamlines for overlapping areas based on the intersection of footprints. Areas with no overlapping imagery will merge the footprints.
RADIOMETRY - Generate seamlines based on the spectral patterns of features within the imagery.
EDGE_DETECTION - Generate seamlines over intersecting areas based on the edges of features in the area.
This method can avoid seamlines cutting through buildings.
context
Optional dictionary. Context contains additional settings that allows users to customize the seamlines generation.
The possible keys for the context dictionary are:
parallelProcessingFactor : The specified number or percentage of processes will be used for the analysis.
computeCandidate : Indicates whether “Compute Mosaic Candidates” will run inside the service task. Default value is False.
maxOverlap : Defines the Maximum Area Overlap for running Compute Mosaic Candidates tool inside the task. Default value is 0.6.
maxLoss : It is Maximum Area Loss Allowed for running Compute Mosaic Candidates tool inside the task. Default value is 0.05.
minRegionSize : Any seamline polygons smaller than this specified threshold will be removed in the seamline result.
pixelSize : Generates seamlines for raster datasets that fall within the specified spatial resolution size.
blendType : Determine how to blend one image into another (Both , Inside , or Outside ) over the seamlines. Inside blends pixels inside the seamline, while Outside blends outside the seamline. Both will blend pixels on either side of the seamline.
blendWidth : Specifies how many pixels will be blended relative to the seamline. Blending (feathering) occurs along a seamline between pixels of overlapping images.
blendUnit : Specifies the unit of measurement for blendWidth . Pixels measures using the number of pixels, and Ground measures using the same units as the image collection.
requestSizeType : Sets the units for requestSize . Pixels modifies requestSize based on the pixel size. This resamples the closest image based on the raster pixel size. Pixel scaling factor modifiers requestSize by specifying a scaling factor. This operation resamples the closest image by multiplying the raster pixel size with the pixel size factor.
requestSize : Specifies the number of columns and rows for resampling. Though the maximum value is 5,000, this value can increase or decreased based on the complexity of your raster data. A greater image resolution provides more detail in the raster dataset but increases the processing time.
minThinnessRatio : Defines how thin a polygon can be before its considered a sliver. This is based on a scale from 0 to 1.0, where a value of 0.0 represents a polygon that’s almost a straight line, and a value of 1.0 represents a polygon that’s a circle.
maxSliverSize : Defines how large a Sliver can be before its considered a polygon. This uses the same scale as minThinnessRatio .
Example:
{“minRegionSize”: 100, “pixelSize”: “”, “blendType”: “Both”, “blendWidth”: None, “blendUnit”: “Pixels”, “requestSizeType”: “Pixels”, “requestSize”: 1000, “minThinnessRatio”: 0.05, “maxSilverSize”: 20 }
Allowed keys are: “minRegionSize”, “pixelSize”, “blendType”, “blendWidth”, “blendUnit”, “requestSizeType”, “requestSize”, “minThinnessRatio”, “maxSilverSize”
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The Imagery layer url
edit_control_points
- arcgis.raster.orthomapping.edit_control_points(image_collection, control_points, *, gis=None, future=False, **kwargs)
This service can be used to append additional ground control point sets to the image collection’s control points. It is recommended that a ground control point (GCP) set should contain one ground control point and multiple tie points. The service tool can also be used to edit tie point sets. The input control points dictionary will always replace the points in the tie points table if the point IDs already exist.
Parameter
Description
image_collection
Required. The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
control_points
Required, a list of control point sets objects.
The schema of control points follows the schema of the mosaic dataset control point table.
The control point object should contain the point geometry, pointID, type, status and the imagePoints. (the imagePoints attribute inside the control points object lists the imageIDs)
– pointID (int) - The ID of the point within the control point table.
– type (int) - The type of the control point as determined by its numeric value
1: Tie Point 2: Ground Control Point. 3: Check Point
– status (int) - The status of the point. A value of 0 indicates that the point will not be used in computation. A non-zero value indicates otherwise.
– imageID (int) - Image identification using the ObjectID from the mosaic dataset footprint table.
Example:
[{“status”: 1,“type”: 2,“x”: -117.0926538,“y”: 34.00704253,“z”: 634.2175,“spatialReference”: {“wkid”: 4326}, // default WGS84“imagePointSpatialReference”: {}, // default ICS“pointId”: 1,“xyAccuracy”: “0.008602325”,“zAccuracy”: “0.015”,“imagePoints”: [{“imageID”: 1,“x”: 2986.5435987557084,“y”: -2042.5193648409431,“u”: 3057.4580682832734,“v”: -1909.1506872159698},{“imageID”: 2,“x”: 1838.2814361401108,“y”: -2594.5280063817972,“u”: 3059.4079724863363,“v”: -2961.292545463305},{“imageID”: 12,“x”: 5332.855578204663,“y”: -2533.2805429751907,“u”: 614.2338676573158,“v”: -165.10836768947297},{“imageID”: 13,“x”: 4932.0895715254455,“y”: -1833.8401744114287,“u”: 616.9396928182223,“v”: -1243.1445126959693}]},……]gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The Imagery layer url
generate_dem
- arcgis.raster.orthomapping.generate_dem(image_collection, out_dem, cell_size, surface_type, matching_method=None, context=None, classify_ground_options=None, *, gis=None, future=False, **kwargs)
Generate a DEM from the image collection. Refer to Interpolate From Point Cloud GP tool for more documentation
Parameter
Description
image_collection
Required. The input image collection that will be used to generate the DEM from. The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
out_dem
This is the output digital elevation model. It can be a url, uri, portal item, or string representing the name of output dem (either existing or to be created.) Like Raster Analysis services, the service can be an existing multi-tenant service URL.
cell_size
Required, The cell size of the output raster dataset. This is a single numeric input. Rectangular cell size such as {“x”: 10, “y”: 10} is not supported. The cell size unit will be the unit used by the image collection’s spatial reference.
surface_type
Required string. Create a digital terrain model or a digital surface model. Refer to “surface_type” parameter of the GP tool.
The available choices are:
DTM - Digital Terrain Model, the elevation is only the elevation of the bare earth, not including structures above the surface.
DSM - Digital Surface Model, the elevation includes the structures above the surface, for example, buildings, trees, bridges.
matching_method
Optional string. The method used to generate 3D points.
ETM-A feature-based stereo matching that uses the Harris operator to detect feature points. It is recommended for DTM generation.
SGM- Produces more points and more detail than the ETM method. It is suitable for generating a DSM for urban areas. This is more computationally intensive than the ETM method1.
MVM (Multi-view image matching (MVM) - is based on the SGM matching method followed by a fusion step in which the redundant depth estimations across single stereo model are merged. It produces dense 3D points and is computationally efficient
References: Heiko Hirschmuller et al., “Memory Efficient Semi-Global Matching,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 1-3, (2012): 371-376.
Refer to the documentation of “matching_method” parameter of the Generate Point Cloud GP tool
context
Optional dictionary. Additional allowed point cloud generation parameter and DEM interpolation parameter can be assigned here.
This dictionary can contain the following keys:
- parallelProcessingFactorSpecifies the number or percentage of
processes will be used for the analysis. The default value is 50% .
- maxObjectSizeA search radium within surface objects, such as
buildings and trees, will be identified. It’s the linear size in map units.
groundSpacing : The ground spacing, in meters, at which the 3D points are generated.
- minAngleThe value, in degrees, that defines the minimum
intersection angle the stereo pair must meet.
- maxAngleThe value, in degrees, that defines the maximum
intersection angle the stereo pair must meet.
- minOverlapSpecifies a minimum overlap threshold that is acceptable,
which is a percentage of overlap between a pair of images. Image pairs with overlap areas smaller than this threshold will receive a score of 0 for this criteria and will descend in the ordered list. The range of values is from 0 to 1.
maxOmegaPhiDif : Specifies the maximum threshold for the Omega/Phi
difference between the image pair. The Omega and Phi values for the image pair are compared, and a difference greater than this threshold will receive a score of 0 and will descend in the ordered list.
maxGSDDif : Specifies the maximum allowable threshold for the ground sample
distance (GSD) between two images in a pair. The resolution ration between the two images will be compared with the threshold value. Image pairs with a GSD greater than this threshold will receive a score of 0 and will descend in the ordered list.
numImagePairs : The number of pairs used to generate 3D points.
adjQualityThreshold : Specifies the minimum acceptable adjustment quality.
The threshold value will be compared to the quality value stored within the stereo model. Image pairs with an adjustment quality less than the specified threshold will receive a score of 0 and will descend in the ordered list. The range of values for the threshold is between 0 and 1.
regenPointCloud : Regenerates the 3D point cloud when set to True.
pointCloudFolder : The point cloud folder to use. This can be one of “DSM”, “DTM”, “LAS”.
For Example:
Point cloud generation parameters -{“maxObjectSize”: 50,“groundSpacing”: None,“minAngle”: 10,“maxAngle”: 70,“minOverlap”: 0.6,“maxOmegaPhiDif”: 8,“maxGSDDif”: 2,“numImagePairs”: 2,“adjQualityThreshold”: 0.2,“regenPointCloud”: False,}DEM interpolation parameters -{“method”: “TRIANGULATION”,“smoothingMethod”: “GAUSS5x5”,“applyToOrtho”: True,“fillDEM”: “https://....”“pointCloudFolder”: “DSM”}Note: The “applyToOrtho” flag can apply the generated DEM back into the mosaic dataset’s geometric function to achieve more accurate orthorectification result. The “fillDEM” flag allows the user to specify an elevation service URL as background elevation to fill the area when elevation model pixels cannot be interpolated from the point cloud.
classify_ground_options
Optional dict. Classify ground points from the input LAS data. This can be used when the surface type is DTM.
The dictionary can contain the following keys:
Classify : the method to use to detect ground points. This value can be one of “standard”, “conservative” or “aggressive”.
standard: This method has a tolerance for slope variation that allows it to capture gradual undulations in the ground’s topography that would typically be missed by the conservative option but not capture the type of sharp reliefs that would be captured by the aggressive option. This is the default.
conservative: When compared to other options, this method uses a tighter restriction on the variation of the ground’s slope that allows it to differentiate the ground from low-lying vegetation such as grass and shrubbery. It is best suited for topography with minimal curvature.
aggressive: This method detects ground areas with sharper reliefs, such as ridges and hill tops, that may be ignored by the standard option. This method is best used in a second iteration of this tool with the ReuseGround option set to 1. Avoid using this method in urban areas or flat, rural areas, as it may result in the misclassification of taller objects — such as utility towers, vegetation, and portions of buildings — as ground.
LowNoise : the distance below the ground that will be used to classify the point to be low-noise points. The unit is meter. The default value is 0.25 meter.
HighNoise : the distance above the ground that will be used to classify the point to be high-noise points. The unit is meter. The default value is 100 meter.
ReuseGround : specifies whether existing ground points will be reclassified or reused. 0 mean reclassify and 1 indicating reuse. The default value is 0.
ReuseLowNoise : specifies whether existing low-noise points will be reused or reclassified. 0 mean reclassify and 1 mean reuse. The default value is 0.
ReuseHighNoise : specifies whether existing high-noise points will be reused or reclassified. 0 mean reclassify and 1 mean reuse. The default value is 0.
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The DEM layer item
generate_orthomosaic
- arcgis.raster.orthomapping.generate_orthomosaic(image_collection, out_ortho, regen_seamlines=True, recompute_color_correction=True, context=None, *, gis=None, future=False, **kwargs)
Function can be used for generating single ortho-rectified mosaicked image from image collection after the block adjustment.
Parameter
Description
image_collection
Required. The input image collection that will be used to generate the ortho-mosaic from. The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
out_ortho
Required. This is the ortho-mosaicked image converted from the image collection after the block adjustment. It can be a url, uri, portal item, or string representing the name of output dem (either existing or to be created.) Like Raster Analysis services, the service can be an existing multi-tenant service URL.
regen_seamlines
Optional, boolean. Choose whether to apply seamlines before the orthomosaic image generation or not. The seamlines will always be regenerated if this parameter is set to True. The user can set the seamline options through the context parameter. If the seamline generation options are not set, the default will be used.
Default value is True
recompute_color_correction
Optional, boolean. Choose whether to apply color correction settings to the output ortho-image or not. Color correction will always be recomputed if this option is set to True. The user can configure the compute color correction settings through the context parameter. If there is no color collection setting, the default will be used.
Default value is True
context
Optional dictionary. Context contains additional environment settings that affect output image. The supported environment settings for this tool are:
The possible key for the context dictionary are:
parallelProcessingFactor : The specified number or percentage of processes will be used for the analysis. The default value is 50%.
orthoMosaicAsOvr : Determines whether to apply the generated orthomosaic image as overview of input image collection. The default value is False.
clippingGeometry : Specifies the extent or clippinggeometry parameter for the Clip raster function. It is used for setting the extent of the output orthomosaic image.The default value is empty.
cellSize : The output raster will have the resolution specified by cell size. The cellSize is specified using the MAXOF , MINOF , or number. The default value is MAXOF.
resamplingMethod : Choose which resampling method to use when creating the raster dataset for download. Available resampling types include: NEARESTNEIGHBOR , BILINEAR , CUBIC , MAJORITY , BILINEAR_PLUS , BILINEAR_GAUSSBLUR , BILINEAR_GAUSSBLUR_PLUS , AVERAGE , MINIMUM , MAXIMUM , VECTOR_AVERAGE . The default value is NEARESTNEIGHBOR .
outSR : The output raster will be projected into the output spatial reference.
seamlinesMethod : Specifies the computation method for running the Build Seamlines tool inside the task. The default value is DISPARITY.
minRegionSize : Any seamline polygons smaller than this specified threshold will be removed in the seamline result.
pixelSize : Generates seamlines for raster datasets that fall within the specified spatial resolution size.
blendType : Determine how to blend one image into another (Both , Inside , or Outside ) over the seamlines. Inside blends pixels inside the seamline, while Outside blends outside the seamline. Both will blend pixels on either side of the seamline.
blendWidth : Specifies how many pixels will be blended relative to the seamline. Blending (feathering) occurs along a seamline between pixels of overlapping images.
blendUnit : Specifies the unit of measurement for blendWidth . Pixels measures using the number of pixels, and Ground measures using the same units as the image collection.
requestSizeType : Sets the units for requestSize . Pixels modifies requestSize based on the pixel size. This resamples the closest image based on the raster pixel size. Pixel scaling factor modifiers requestSize by specifying a scaling factor. This operation resamples the closest image by multiplying the raster pixel size with the pixel size factor.
requestSize : Specifies the number of columns and rows for resampling. Though the maximum value is 5,000, this value can increase or decreased based on the complexity of your raster data. A greater image resolution provides more detail in the raster dataset but increases the processing time.
minThinnessRatio : Defines how thin a polygon can be before its considered a sliver. This is based on a scale from 0 to 1.0, where a value of 0.0 represents a polygon that’s almost a straight line, and a value of 1.0 represents a polygon that’s a circle.
maxSliverSize : Defines how large a Sliver can be before its considered a polygon. This uses the same scale as minThinnessRatio .
skipX and skipY : Specifies the X skip factor and the Y skip factor for running the Build Pyramids And Statistics tool inside the task as part of color correction workflow. The default values are both 1.
overwriteStats : Specifies the Skip Existing parameter for running the Build Pyramids And Statistics tool inside the task as part of color correction workflow. The default value isFalse , and statistics will not be recalculated if they exist.
colorCorrectionMethod : Specifies the balance method for running the Color Balance Mosaic Dataset tool inside the task. The default value is DODGING .
dodgingSurface : Specifies the color surface type for running the Color Balance Mosaic Dataset tool inside the task. The default value is SINGLE_COLOR .
targetImage : Specifies the target raster for running the Color Balance Mosaic Dataset tool inside the task. The default value is empty.
applyColorCorrection : Indicates whether or not to apply color correction to the image collection. The default value is True .
Example:
{“outSR”: {“wkid”: 3516},“extent”: {“xmin”: 470614.263139,“ymin”: 8872849.409968,“xmax”: 532307.351827,“ymax”: 8920205.372412,“spatialReference”: {“wkid”: 32628}},“clippingGeometry”: {},“orthoMosaicAsOvr”: False,“seamlinesMethod”: “VORONOI”,“minRegionSize”: 100,“pixelSize”: “”,“blendType”: “Both”,“blendWidth”: None,“blendUnit”: “Pixels”,“requestSize”: 1000,“minThinnessRatio”: 0.05,“maxSliverSize”: 20“colorCorrectionMethod”: “DODGING”,“dodgingSurface”: “Single_Color”,“referenceImg”: {“url”: “https://...”},“skipX”: 10,“skipY”: 10,“overwriteStats”: “OVERWRITE”}gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The Orthomosaicked Imagery layer item
generate_report
- arcgis.raster.orthomapping.generate_report(image_collection, report_format='PDF', *, gis=None, future=False, **kwargs)
This function is used to generate orthomapping report with image collection that has been block adjusted. The report would contain information about the quality of the adjusted images, the distribution of the control points, etc. The output of this service tool is a downloadable html page.
Parameter
Description
image_collection
Required. The input image collection that should be used to generate a report from.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
report_format
Type of the format to be generated. Possible PDF, HTML. Default - PDF
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
The URL of a single html webpage that is a formatted orthomapping report
query_camera_info
- arcgis.raster.orthomapping.query_camera_info(camera_query=None, *, gis=None, future=False, **kwargs)
This service tool is used to query specific or the entire digital camera database. The digital camera database contains the specs of digital camera sensors that were used to capture drone images.
Parameter
Description
camera_query
Optional Dictionary or String. A dictionary or a string representing the SQL query statement to query the specifications of digital camera sensors that are used to capture drone images. The digital camera database can be queried using the fields Make, Model, Focallength, Columns, Rows, PixelSize.
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
Dictionary/Data Frame representing the camera database
# Example 1: Query camera properties for camera Rollei RCP-8325 in dictionary format. camera_info = query_camera_info(camera_query={"Make":"Rollei", "Model":"RCP-8325"}) # Example 2: Query camera properties for camera Rollei RCP-8325 in string format. camera_info = query_camera_info(camera_query="Make='Rollei' and Model='RCP-8325'")
query_control_points
- arcgis.raster.orthomapping.query_control_points(image_collection, query, *, gis=None, future=False, **kwargs)
Query for control points in an image collection. It allows users to query among certain control point sets that has ground control points inside.
Parameter
Description
image_collection
Required, the input image collection on which to query the the control points.
The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
query
Required string. a SQL statement used for querying the point;
Example:
“pointID > 100”
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
A dictionary object
reset_image_collection
- arcgis.raster.orthomapping.reset_image_collection(image_collection, *, gis=None, future=False, **kwargs)
Reset the image collection. It is used to reset the image collection to its original state. The image collection could be adjusted during the orthomapping workflow and if the user is not satisfied with the result, they will be able to clear any existing adjustment settings and revert the images back to un-adjusted state
Parameter
Description
image_collection
Required, the input image collection to reset The image_collection can be a Mission object, an image service URL or portal Item or a datastore URI.
The image_collection must exist.
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
A boolean indicating whether the reset was successful or not
compute_spatial_reference_factory_code
- arcgis.raster.orthomapping.compute_spatial_reference_factory_code(latitude, longitude)
Computes spatial reference factory code. This value may be used as out_sr value in create image collection function
Parameter
Description
latitude
latitude value in decimal degrees that will be used to compute UTM zone
longitude
longitude value in decimal degrees that will be used to compute UTM zone
- Returns:
factory_code : spatial reference factory code
query_exif_info
- arcgis.raster.orthomapping.query_exif_info(input_images, *, gis=None, future=False, **kwargs)
The query_exif_info reads the Exif header metadata from single or multiple images in shared data store. The Exif metadata is usually stored in drone image files. Some common Exif metadata information are GPS locations, camera model, focal length, and more.
Parameter
Description
input_images
Required String/list of Strings. The input images could be a single image path, list of image paths, or a folder path, or a list of folder paths. The image file paths can also be server data store path.
Eg:
“servernamedroneimagefolderimage_file.jpg”
“/cloudStores/S3DataStore/yvwd13”
“/fileShares/drones/SampleEXIF/YUN_0040.jpg”
[“/fileShares/drones/SampleEXIF/DJI_0002.JPG”, “/fileShares/drones/SampleEXIF/YUN_0040.jpg”]
[“/cloudStores/S3DataStore/yvwd13”, “/cloudStores/S3DataStore/BogotaFarm”]
gis
Optional
GIS. The GIS on which this tool runs. If not specified, the active GIS is used.- Returns:
A dictionary object
Project
- class arcgis.raster.orthomapping.Project(project=None, definition=None, *, gis=None, **kwargs)
Project represents an Orthomapping Project Item in the portal.
Usage:
arcgis.raster.Project(project, gis=gis)Parameter
Description
project
Required string or Orthomapping Project Item
Example:
project = “OM_project”om_item = gis.content.get(“85a54236c6364a88a7c7c2b1a31fd901”)project = om_itemdefinition
Optional dictionary. Custom project definition.
gis
Optional
GIS. Repesents the GIS object of the Orthomapping Project item.# Example Usage project = Project('om_proj', gis=gis) # Example Usage om_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901") project = Project(om_item, gis=gis)
- add_mission(image_list, mission_name=None, image_collection=None, raster_type_name=None, raster_type_params=None, out_sr=None, context=None)
Add missions to the orthomapping project item. You can add imagery from one or more drone flights to your orthomapping project item.
Parameter
Description
image_list
Required, the list of input images to be added to the image collection being created. This parameter can be a list of image paths or a path to a folder containing the images
The function can create hosted imagery layers on enterprise from local raster datasets by uploading the data to the server.
mission_name
Optional string. The name of the mission.
image_collection
Optional string, the name of the image collection to create.
The image collection can be an existing image service, in which the function will create a mosaic dataset and the existing hosted image service will then point to the new mosaic dataset.
If the image collection does not exist, a new multi-tenant service will be created.
This parameter can be the Item representing an existing image_collection or it can be a string representing the name of the image_collection (either existing or to be created.)
The image collection will be created in the same folder as the one created by the create_project method
raster_type_name
Optional string. The name of the raster type to use for adding data to the image collection.
Example:
“UAV/UAS”
raster_type_params
Optional dict. Additional
raster_typespecific parameters.The process of add rasters to the image collection can be controlled by specifying additional raster type arguments.
The raster type parameters argument is a dictionary.
The dictionary can contain productType, processingTemplate, pansharpenType, Filter, pansharpenWeights, ConstantZ, dem, zoffset, CorrectGeoid, ZFactor, StretchType, ScaleFactor, ValidRange
Please check the table below (Supported Raster Types), for more details about the product types, processing templates, pansharpen weights for each raster type.
Possible values for pansharpenType - [“Mean”, “IHS”, “Brovey”, “Esri”, “Mean”, “Gram-Schmidt”]
Possible values for filter - [None, “Sharpen”, “SharpenMore”]
Value for StretchType dictionary can be as follows:
“None”
“MinMax; <min>; <max>”
“PercentMinMax; <MinPercent>; <MaxPercent>”
“StdDev; <NumberOfStandardDeviation>”
Example: {“StretchType”: “MinMax; <min>; <max>”}
Value for ValidRange dictionary can be as follows:
“<MaskMinValue>, <MaskMaxValue>”
Example: {“ValidRange”: “10, 200”}
Example:
{“productType”:”All”,”processingTemplate”:”Pansharpen”, “pansharpenType”:”Gram-Schmidt”,”filter”:”SharpenMore”, “pansharpenWeights”:”0.85 0.7 0.35 1”,”constantZ”:-9999}
context
Optional dict. The context parameter is used to provide additional input parameters.
Syntax: {“image_collection_properties”: {“imageCollectionType”:”Satellite”},”byref”:True}
Use
image_collection_propertieskey to set value for imageCollectionType.Note
The “imageCollectionType” property is important for image collection that will later on be adjusted by orthomapping system service. Based on the image collection type, the orthomapping system service will choose different algorithm for adjustment. Therefore, if the image collection is created by reference, the requester should set this property based on the type of images in the image collection using the following keywords. If the imageCollectionType is not set, it defaults to “UAV/UAS”
If
byrefis set to ‘True’, the data will not be uploaded. If it is not set, the default is ‘False’The context parameter can also be used to specify whether to build overviews, build footprints, to specify pixel value that represents the NoData etc.
Example:
{“buildFootprints”:True,“footprintsArguments”:{“method”:”RADIOMETRY”,”minValue”:1,”maxValue”:5,“shrinkDistance”:50,”skipOverviews”:True,”updateBoundary”:True,“maintainEdge”:False,”simplification”:None,”numVertices”:20,“minThinnessRatio”:0.05,”maxSliverSize”:20,”requestSize”:2000,“minRegionSize”:100},“defineNodata”:True,“noDataArguments”:{“noDataValues”:[500],”numberOfBand”:99,”compositeValue”:True},“buildOverview”:True}The context parameter can be used to add new fields when creating the image collection.
Example:
{“fields”: [{“name”: “cloud_cover”, “type”: “Long”},{“name”: “cloud_shadow_count”, “type”: “Long”}]}- Returns:
Mission object
- delete()
The
deletemethod deletes the project item from the portal and all the associated products.- Returns:
A boolean indicating whether the deletion was successful or not
- get_mission(name)
Returns a Mission object with the name specified using the name parameter.
Parameter
Description
name
Required string. The name of the Mission.
- Returns:
Mission object
- property item
The
itemproperty returns the portal item associated with the Project.- Returns:
A portal item
Mission
- class arcgis.raster._mission.Mission(mission_name, project)
Mission represents a mission in an Orthomapping Project.
Note
This class is not created by users directly. An instance of this class is returned as output for get_mission() and add_mission() methods on the Project class of arcgis.raster.orthomapping module.
Parameter
Description
mission_name
Required string representing the mission name.
Example:
mission_name=’Mission_Yucaipa’
project
Required Project object or an Orthomapping Project portal item. The orthomapping project to which the mission belongs to.
# Example Usage 1 om_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901") project = Project(om_item, gis=gis) mission_1 = project.add_mission(image_list, mission_name="mission_name", image_collection="img_collection", raster_type_name="UAV/UAS", raster_type_params=raster_type_params) # Example Usage 2 om_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901") project = Project(om_item, gis=gis) mission_1 = project.get_mission("mission_name")
- add_image(input_rasters, raster_type_name=None, raster_type_params=None, context=None)
Add a collection of images to existing image collection of the mission. It provides provision to specify image collection properties through context parameter.
It can be used when new data is available to be included in the same mission of the orthomapping project. When new data is added to the image collection the entire image collection must be reset to the original state.
Parameter
Description
input_rasters
Required, the list of input images to be added to the image collection being created. This parameter can be a list of image paths or a path to a folder containing the images
The function can create hosted imagery layers on enterprise from local raster datasets by uploading the data to the server.
raster_type_name
Optional string. The name of the raster type to use for adding data to the image collection.
Choice list:
[“Aerial”, “ASTER”, “DMCII”, “DubaiSat-2”, “GeoEye-1”, “GF-1 PMS”, “GF-1 WFV”,“GF-2 PMS”, “GRIB”, “HDF”, “IKONOS”, “Jilin-1”, “KOMPSAT-2”, “KOMPSAT-3”,“Landsat 1-5 MSS”, “Landsat 4-5 TM”, “Landsat 7 ETM+”, “Landsat 8”, “Landsat 9”,“NetCDF”, “PlanetScope”, “Pleiades-1”, “Pleiades NEO”, “QuickBird”, “RapidEye”,“Raster Dataset”, “ScannedAerial”, “Sentinel-2”, “SkySat”, “SPOT 5”, “SPOT 6”,“SPOT 7”, “Superview-1”, “Tiled Imagery Layer”, “UAV/UAS”, “WordView-1”,“WordView-2”, “WordView-3”, “WordView-4”, “ZY3-SASMAC”, “ZY3-CRESDA”]Example:
“QuickBird”
raster_type_params
Optional dict. Additional
raster_typespecific parameters.The process of add rasters to the image collection can be controlled by specifying additional raster type arguments.
The raster type parameters argument is a dictionary.
Syntax:
{“gps”: [[“image1.jpg”, “10”, “2”, “300”], [“image2.jpg”, “10”, “3”, “300”], [“image3.jpg”, “10”, “4”, “300”]], “cameraProperties”: {“Maker”: “Canon”, “Model”: “5D Mark II”, “FocalLength”: 20, “PixelSize”: 10, “x0”: 0, “y0”: 0, “columns”: 4000, “rows”: 3000}, “constantZ”: 300,”isAltitudeFlightHeight”: “True”,”dem”: {“url”:
https://...}The dictionary can contain productType, processingTemplate, pansharpenType, Filter, pansharpenWeights, ConstantZ, dem, zoffset, CorrectGeoid, ZFactor, StretchType, ScaleFactor, ValidRange
Please check the table below (Supported Raster Types), for more details about the product types, processing templates, pansharpen weights for each raster type.
Possible values for pansharpenType - [“Mean”, “IHS”, “Brovey”, “Esri”, “Mean”, “Gram-Schmidt”]
Possible values for filter - [None, “Sharpen”, “SharpenMore”]
Value for StretchType dictionary can be as follows:
“None”
“MinMax; <min>; <max>”
“PercentMinMax; <MinPercent>; <MaxPercent>”
“StdDev; <NumberOfStandardDeviation>”
Example: {“StretchType”: “MinMax; <min>; <max>”}
Value for ValidRange dictionary can be as follows:
“<MaskMinValue>, <MaskMaxValue>”
Example: {“ValidRange”: “10, 200”}
Example:
{“productType”:”All”,”processingTemplate”:”Pansharpen”, “pansharpenType”:”Gram-Schmidt”,”filter”:”SharpenMore”, “pansharpenWeights”:”0.85 0.7 0.35 1”,”constantZ”:-9999}
context
Optional dict. The context parameter is used to provide additional input parameters.
Syntax:
{“image_collection_properties”: {“imageCollectionType”:”Satellite”},”byref”:’True’}
Use
image_collection_propertieskey to set value for imageCollectionType.Note
The “imageCollectionType” property is important for image collection that will later on be adjusted by orthomapping system service. Based on the image collection type, the orthomapping system service will choose different algorithm for adjustment. Therefore, if the image collection is created by reference, the requester should set this property based on the type of images in the image collection using the following keywords. If the imageCollectionType is not set, it defaults to “UAV/UAS”
If byref is set to ‘True’, the data will not be uploaded. If it is not set, the default is ‘False’
- Returns:
The imagery layer url
- delete()
The
deletemethod deletes the Mission and all the associated products.- Returns:
A boolean indicating whether the deletion was successful or not
- delete_image(where)
delete_imageallows users to remove existing images from the image collection of a mission.Parameter
Description
where
Required string. A SQL
whereclause for selecting the images to be deleted from the image collection- Returns:
The imagery layer url
- delete_product(product)
The
delete_productmethod deletes the product specified by the product parameter.Parameter
Description
product
Required string, the product that needs to be deleted from the mission. It could be “dsm”, “dtm”, “ortho”.
- Returns:
A boolean indicating whether the deletion was successful or not
- property image_collection
The
image_collectionproperty returns the image collection associated with the mission- Returns:
image collection item
- property image_count
The
image_countproperty returns the number of images in the mission- Returns:
An integer representing the number of images
- property mission_date
The
mission_dateproperty returns the date of the mission.- Returns:
A datetime object representing the mission date