arcgis.raster.realitymapping module

Bases: object

Project represents an Realitymapping Project Item in the portal.

Usage: arcgis.raster.Project(project, gis=gis)

# Example Usage

project = Project('rm_proj', gis=gis)

# Example Usage

rm_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901")
project = Project(rm_item, gis=gis)

# Using the SensorType and ScenarioType enums
from arcgis.raster.realitymapping import SensorType, ScenarioType
project = Project(
            'rm_proj',
            sensor_type=SensorType.AERIAL_DIGITAL,
            scenario_type=ScenarioType.AERIAL_NADIR,
            gis=gis
        )

create_mission(image_list: list[str | Item] = None, image_collection_name: str | dict[str, str] | None = None, mission_name: str | None = None, raster_type_name: str | None = None, raster_type_params: dict | None = None, out_sr: dict | None = None, settings: dict | None = None, context: dict | None = None, *, gis: GIS | None = None, future: bool | None = False, **kwargs)

Creates a new mission in the realitymapping project. The mission is created with the specified image collection, raster type, and settings.

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. This can be datastore paths, local paths, or a list of Item objects. The function can create hosted imagery layers on enterprise from local raster datasets by uploading the data to the server.
image_collection_name	Optional string or dictionary. The name of the image collection to be created. If a string is provided, it will be used as the portal name for the image collection. If a dictionary is provided, it should contain the keys ‘service_name’ and ‘portal_name’. The ‘service_name’ must be unique. For e.g.: {“service_name”: “my_image_service”, “portal_name”: “My Image Collection”}
mission_name	Optional string. The name of the mission to be created. If a name is not provided, a default name will be generated in the format “mission_<random_id>”.
raster_type_name	Optional string. The name of the raster type used. Refer to create_image_collection() for supported raster types.
raster_type_params	Optional dict. Additional raster_type specific 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 the following keys: productType processingTemplate pansharpenType Filter pansharpenWeights ConstantZ dem zoffset CorrectGeoid ZFactor StretchType ScaleFactor ValidRange For Supported Raster Types, please refer to the documentation for create_image_collection() for 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>”} ValidRange can be specified as: “<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}
out_sr	Optional dictionary. The spatial reference to be used for this mission.
settings	Optional dictionary. The settings for this mission. If not provided, the project settings will be inherited. A subset of the settings can also be provided but the structure of the settings dictionary must be preserved. An example is provided below. For the entire list of settings, please refer to the
context	Optional dict. The context parameter is used to provide additional input parameters. Syntax: {“image_collection_properties”: {“imageCollectionType”:”Satellite”},”byref”:True} Use image_collection_properties key 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’ 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”}]}
gis	Optional GIS . The GIS on which this tool runs If not specified, the active GIS is used.

Returns:

Mission object

# Example settings dictionary:

settings = {
    "adjustSettings": {
        "maxResidual": 5.0,
        "focalLength": False,
        "locationAccuracy": "LOW"
    },
    "processingSettings": {
        "dsm": {
            "dsm": {
                "format": "TIFF",
                "outputType": "TILED",
                "resampling": "BILINEAR"
            }
        },
        "dtm": {
            "dtm": {
                "mask": "",
                "extent": "",
                "format": "CRF",
                "fillDEM": "",
                "cellsize": "NaN",
                "lowNoise": 0.25,
                "highNoise": 100.0,
                "compression": "NONE",
                "reuseGround": False
            }
        }
    }
}

# Example Usage

mission = project.create_mission(
                    image_list=["/path/to/image1.tif", "/path/to/image2.tif"],
                    image_collection_name={"service_name": "my_image_service", "portal_name": "My Image Collection"},
                    mission_name="My Mission",
                    raster_type_name="UAV/UAS",
                    raster_type_params={
                            "isAltitudeFlightHeight": "False",
                            "averagezdem": {"url": "https://elevation3d.arcgis.com/arcgis/rest/services/WorldElevation3D/Terrain3D/ImageServer"}
                        },
                    out_sr={"wkid": 4326}
        )

delete()

The delete method 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: str)

Returns a Mission object with the name specified using the name parameter.

Parameter	Description
name	Required string. The name of the Mission. The name of the mission is case sensitive.

Returns:

The imagery layer url

property group

The group property returns the group associated with the project.

Returns:

A Group object representing the group associated with the project

property item

The item property returns the portal item associated with the project.

Returns:

A portal item

merge_missions(missions, output_mission_name: str | None = None, mission_settings: str | None = None, *, gis: GIS | None = None, future: bool | None = False, **kwargs)

Merges multiple missions and creates a new mission in the realitymapping project.

Parameter	Description
missions	Required list of Mission objects. The missions to be merged.
output_mission_name	Optional string. The name of the output mission to be created. If not provided, a default name will be generated in the format “mission_<random_id>”.
mission_settings	Optional dictionary. The settings for the new mission. If not provided, the project settings will be inherited. A subset of the settings can also be provided but the structure of the settings dictionary must be preserved. Refer to the settings property of the Project class for more details on the structure of the settings dictionary.
gis	Optional GIS. The GIS on which this tool runs. If not provided, the active GIS will be used.

Returns:

A Mission object representing the merged mission.

# Example Usage

mission1 = project.get_mission("mission_1")
mission2 = project.get_mission("mission_2")

merged_mission = project.merge_missions(
    missions=[mission1, mission2],
    output_mission_name="Merged Mission",
    mission_settings={
        "adjustSettings": {
            "maxResidual": 5,
            "focalLength": False,
            "locationAccuracy": "LOW"
        }
    }
)

property mission_count

The mission_count property returns the number of missions associated with the project

Returns:

An integer representing the number of missions

property missions

The missions property returns all the missions associated with the project

Returns:

A list of missions of the realitymapping project

property settings

The settings property returns the processing settings of the project.

Returns:

A dictionary representing the processing settings of the project

property spatial_reference

The spatial_reference property returns the spatial reference of the project.

Returns:

A dictionary representing the spatial reference of the project

Bases: object

Mission represents a mission in an Realitymapping Project.

# Example Usage 1

rm_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901")
project = Project(rm_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

rm_item = gis.content.get("85a54236c6364a88a7c7c2b1a31fd901")
project = Project(rm_item, gis=gis)

mission_1 = project.get_mission("mission_name")

add_image(input_rasters: list, raster_type_name: str | None = None, raster_type_params: dict[str, Any] | None = None, context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = False, **kwargs)

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 realitymapping 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_type specific 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_properties key to set value for imageCollectionType. Note The “imageCollectionType” property is important for image collection that will later on be adjusted by realitymapping system service. Based on the image collection type, the realitymapping 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

alter_processing_states(new_states: dict[str, Any], *, gis: GIS | None = None, future: bool = 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
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

compute_control_points(reference_image=None, image_location_accuracy: str = 'High', context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = 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
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_sensor_model(mode: str = 'Quick', location_accuracy: str = 'High', context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = 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
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. 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

delete()

The delete method deletes the Mission and all the associated products.

Returns:

A boolean indicating whether the deletion was successful or not

delete_image(where: str, context: dict | None = None, *, gis: GIS | None = None, future: bool = False, estimate: bool | None = False, **kwargs)

delete_image allows users to remove existing images from the image collection (mosaic dataset) of a mission.

Parameter	Description
where	Required string. A SQL where clause for selecting the images to be deleted from the image collection

Returns:

The imagery layer url

edit_control_points(control_points: list[dict[str, Any]], *, gis: GIS | None = None, future: bool = 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
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_orthomosaic(out_ortho=None, regen_seamlines: bool = True, recompute_color_correction: bool = True, context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = False, **kwargs)

Function can be used for generating single ortho-rectified mosaicked image from image collection after the block adjustment.

Parameter	Description
out_ortho	Required String or dict. 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.) If passed as a dict, the following keys can be set: - service_name: The name for the output ortho-mosaicked image service. - portal_name: The name for the portal item for this image service. If this product has already been created, the tool will overwrite it instead. 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: Output Spatial Reference (outSR)-the output features will be projected into the output spatial reference. Extent (extent) - extent that would clip or expand the output image Cell Size (cellSize) - The output raster will have the resolution specified by cell size. Compute Seamlines (seamlinesMethod) - Default. Clipping Geometry (clippingGeometry) - Clips the orthomosaic image to an area of interest defined by the geometry. Orthomosaic As Overview (orthoMosaicAsOvr) - Adds the orthomosaic as an overview of the image collection. Compute Color Correction (colorcorrectionMethod) — Default. 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://...”}, “skipRows”: 10, “skipCols”: 10, “reCalculateSats”: “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(report_format: str = 'PDF', *, gis: GIS | None = None, future: bool = False, **kwargs)

This function is used to generate realitymapping 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
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 realitymapping report

get_processing_states(*, gis: GIS | None = None, future: bool = False, **kwargs)

Retrieve the processing states of the image collection

Parameter	Description
gis	Optional GIS . The GIS on which this tool runs. If not specified, the active GIS is used.

Returns:

The result will be the current states dictionary

property image_collection

The image_collection property returns the image collection associated with the mission.

Returns:

image collection item.

property image_count

The image_count property returns the number of images in the mission.

Returns:

An integer representing the number of images.

match_control_points(control_points: list[dict[str, Any]], similarity: str = 'High', context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = 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
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. 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

property mission_date

The mission_date property returns the creation date & time of the mission.

Returns:

A datetime object representing the mission date & time.

property mission_id

The mission_id property returns the ID of the mission.

Returns:

A string representing the mission ID.

property products

The products property returns all the products associated with the mission.

Returns:

A list of products of the mission.

query_camera_info(*, gis: GIS | None = None, future: bool = 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	Required String. This is a SQL query statement that can be used to filter a portion of the digital camera database. Digital camera database can be queried using the fields Make, Model, Focallength, Columns, Rows, PixelSize. Example: “Make=’Rollei’ and Model=’RCP-8325’”
gis	Optional GIS . The GIS on which this tool runs. If not specified, the active GIS is used.

Returns:

Data Frame representing the camera database

query_control_points(query: str, *, gis: GIS | None = None, future: bool = 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
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

query_exif_info(*, gis: GIS | None = None, future: bool = 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

reconstruct_surface(scenario: str | None = 'DRONE', forward_overlap: int | None = None, sideward_overlap: int | None = None, quality: str | None = 'ULTRA', area_of_interest: str | FeatureLayer | None = 'AUTO', waterbody_features: FeatureLayer | None = None, correction_feature: FeatureLayer | None = None, reconstruct_options: str | None = None, output_dsm_name: str | None = None, output_true_ortho_name: str | None = None, output_dsm_mesh_name: str | None = None, output_point_cloud_name: str | None = None, output_mesh_name: str | None = None, output_dtm_name: str | None = None, context: dict[str, Any] | None = None, *, gis: GIS | None = None, future: bool = False, **kwargs)

The reconstruct_surface generates a digital surface model (DSM), true orthos, 2.5D meshes, 3D meshes, and point clouds from adjusted imagery.

Parameter	Description
scenario	Optional String. Specifies the type of imagery that will be used to generate the output products. DRONE: The input imagery will be defined as having been acquired with drones or terrestrial cameras. AERIAL_NADIR: The input imagery will be defined as having been acquired with large, photogrammetric camera systems. AERIAL_OBLIQUE: The input imagery will be defined as having been acquired with oblique camera systems. SATELLITE: The input imagery will be defined as having been acquired with a satellite.
forward_overlap	Optional Integer. The forward (in-strip) overlap percentage that will be used between the images. The default is 60. This parameter is enabled when the scenario parameter is set to AERIAL_NADIR.
sideward_overlap	Optional Integer. The sideward (cross-strip) overlap percentage that will be used between the images. The default is 30. This parameter is enabled when the scenario parameter is set to AERIAL_NADIR.
quality	Optional String. Specifies the quality of the final product. ULTRA - Input images will be used at their original (full) resolution. HIGH - Input images will be downsampled two times.
area_of_interest	Optional FeatureLayer or String. The area of interest that will be used to select images for processing. The area of interest can be computed automatically or defined using an input feature. If the value contains 3D geometries, the z-component will be ignored. If the value includes overlapping features, the union of these features will be computed. NONE - All images will be used in processing. AUTO - The processing extent will be calculated automatically. This is the default.
waterbody_features	Optional FeatureLayer. A polygon that will define the extent of large water bodies. For the best results, use a 3D feature.
correction_features	Optional FeatureLayer. A polygon that will define the extent of all surfaces that are not water bodies. The value must be a 3D feature.
reconstruction_options	Optional dict or shared data path (this path must be accessible by the server). This specifies the values for the tool parameters. If this parameter is specified, the properties of the file or dictionary will set the default values for the remaining optional parameters. The list of keywords and an example of this JSON can be found here: Reconstruct Surface tool
output_dsm_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
output_true_ortho_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
output_dsm_mesh_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
output_point_cloud_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
output_mesh_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
output_dtm_name	Optional String. You can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. If this product has already been created, the tool will overwrite it instead. A RuntimeError is raised if a service by that name already exists.
context	Context contains additional settings that affect task execution. context parameter overwrites values set through arcgis.env parameter This function has the following settings: Extent (extent): A bounding box that defines the analysis area. Example: {“extent”: {“xmin”: -122.68, “ymin”: 45.53, “xmax”: -122.45, “ymax”: 45.6, “spatialReference”: {“wkid”: 4326}}} Cell Size (cellSize): The output raster will have the resolution specified by cell size. Example: {‘cellSize’: 11} or {‘cellSize’: {‘url’: <image_service_url>}} or {‘cellSize’: ‘MaxOfIn’} Parallel Processing Factor (parallelProcessingFactor): controls Raster Processing (CPU) service instances. Example: Syntax example with a specified number of processing instances: {“parallelProcessingFactor”: “2”} Syntax example with a specified percentage of total processing instances: {“parallelProcessingFactor”: “60%”} Output DSM product settings: controls the environment variables for creating the DSM product. Example: Syntax example with a specified number of processing instances: {key: {“outputType”: “Tiled”, “compression”: “JPEG 75”, “resamplingMethod”: “NEAREST”, “cellSize”: 10, “noData”: 0}} Output True Ortho product settings: controls the environment variables for creating the DSM product. Example: Syntax example with a specified number of processing instances: {“true_ortho”: {“outputType”: “Mosaic”, “compression”: “JPEG 75”, “resamplingMethod”: “NEAREST”, “cellSize”: 10, “noData”: 0}}
gis	Optional, the GIS on which this tool runs. If not specified, the active GIS is used.
future	Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns:

Named Tuple

reset_image_collection(*, gis: GIS | None = None, future: bool = 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
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

property settings

The settings property returns the processing settings of the project.

Returns:

A dictionary representing the processing settings of the project.

property workspace

The workspace property returns the workspace created for the reality mapping mission on the server.

Returns:

A string representing the workspace name.