Binary thresholding

Thresholding

The binary Threshold function produces the binary image. It uses the Otsu method and assumes the input image to have a bi-modal histogram.

JSON | Python

Analysis

Heat index

PythonAdaptor

Calculates apparent temperature based on ambient temperature and relative humidity.

Analysis

Kernel density

KernelDensity

Calculates a magnitude-per-unit area from point or polyline features using a kernel function to fit a smoothly tapered surface to each point or polyline.

Analysis

NDVI

NDVI

The Normalized Difference Vegetation Index (NDVI) is a standardized index that allows you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band. For more information, see NDVI function.

JSON | Python

Analysis

NDVI Colorized

NDVIColorized

Applies the NDVI function on the input image, and then uses a color map or color ramp to display the result.

Analysis

Tassel Cap

TasselCap

The Tasseled Cap (Kauth-Thomas) transformation is designed to analyze and map vegetation phenomenology and urban development changes detected by various satellite sensor systems. It is known as the Tasseled Cap transformation due to the shape of the graphical distribution of data.

JSON | Python

Analysis

Weighted overlay

WeightedOverlay

The WeightedOverlay function allows you to overlay several rasters using a common measurement scale and weights each according to its importance. For more information, see Weighted Overlay function.

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Analysis

Weight sum

WeightSum

The WeightedSum function allows you to overlay several rasters, multiplying each by their given weight and summing them together. For more information, see Weighted Sum function.

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Analysis

Wind chill

PythonAdaptor

Wind chill is a way to measure how cold it feels when wind is taken into account.

Analysis

Contrast and brightness

ContrastBrightness

The ContrastBrightness function enhances the appearance of raster data (imagery) by modifying the brightness or contrast within the image. This function works on 8-bit input raster only.

JSON | Python

Appearance

Convolution

Convolution

The Convolution function performs filtering on the pixel values in an image, which can be used for sharpening an image, blurring an image, detecting edges within an image, or other kernel-based enhancements. For more information, see Convolution function.

JSON | Python

Appearance

Line detection horizontal

Convolution

Detects edges along horizontal lines.

Appearance

Line detection vertical

Convolution

Detects edges along vertical lines.

Appearance

Line detection left diagonal

Convolution

Detects edges along diagonal lines moving from lower right to upper left.

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Line detection right diagonal

Convolution

Detects edges along diagonal lines lower left to upper right.

Appearance

Gradient north

Convolution

Edge detection along northern gradients.

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Gradient west

Convolution

Edge detection along western gradients.

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Gradient east

Convolution

Edge detection along eastern gradients.

Appearance

Gradient south

Convolution

Edge detection along southern gradients.

Appearance

Gradient north-east

Convolution

Edge detection along north-eastern gradients.

Appearance

Gradient north-west

Convolution

Edge detection along north-western gradients.

Appearance

Smoothing

Convolution

Filters data by reducing local variation and removing noise. The effect is that the high and low values within each neighborhood are averaged out, reducing the extreme values in the data.

Appearance

Smoothing 3x3

Convolution

Filters data by reducing local variation and removing noise. Uses a low-pass 3 by 3 filter to perform the smoothing.

Appearance

Smoothing 5x5

Convolution

Filters data by reducing local variation and removing noise. Uses a low- pass 5 by 5 filter to perform the smoothing.

Appearance

Sharpen

Convolution

Accentuates the comparative difference in the values with its neighbors.

Appearance

Sharpen More

Convolution

Accentuates the value even more tan the Sharpen operator.

Appearance

Sharpening 3x3

Convolution

A high-pass filter using a 3 by 3 kernel.

Appearance

Sharpening 5x5

Convolution

A high-pass filter using a 5 by 5 kernel.

Appearance

Laplacian 3x3

Convolution

Laplacian filters are often used for edge detection to an image that has first been smoothed to reduce its sensitivity to noise. This uses a 3 by 3 filter.

Appearance

Laplacian 5x5

Convolution

Laplacian filters are often used for edge detection to an image that has first been smoothed to reduce its sensitivity to noise. This uses a 5 by 5 filter.

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Sobel Horizontal

Convolution

Used for horizontal edge detection.

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Sobel Vertical

Convolution

Used for vertical edge detection.

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Point Spread

Convolution

The point spread function portrays the distribution of light from a point source through a lense. This will introduce a slight blurring effect.

Appearance

Pansharpening

Pansharpening

The Pansharpening function uses a higher-resolution panchromatic image or raster band to fuse with a lower-resolution, multiband raster dataset to increase the spatial resolution of the multiband image.

JSON | Python

Appearance

Statistics and Histogram

StatisticsHistogram

The Statistics and Histogram function is used to define the statistics and histogram of a raster. You can insert this function at the end of the function chain to describe the statistics and histogram of a raster function template (RFT). This may be needed to control the default display of the processing result, especially when defining a function chain that contains many functions.

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Appearance

Stretch (contrast)

Stretch

Calculates the focal statistics for each pixel of an image, base on a defined focal neighborhood.

JSON | Python

Appearance

Classify

Classify

The Classify function classifies a segmented raster to a categorical raster.

JSON | Python

Classification

Maximum Likelihood Classification

MLClassify

The MLClassify function allows you to perform a supervised classification using the maximum likelihood classification algorithm. The hosting ArcGIS Server needs to have a Spatial Analyst license.

JSON | Python

Classification

Region grow

Region Grow

The Region Grow function groups neighboring pixels into groups depending on the specified radius from the seed point. The group of pixels or object is assigned a specified fill value.

Classification

Segmentation

SegmentMeanShift

The SegmentMeanShift function produces a segmented output. Pixel values in the output image represent the converged RGB colors of the segment. The input raster needs to be a 3-band 8-bit image. If the image service is not a 3-band 8-bit unsigned image, you can use the Stretch function before the SegmentMeanShift function.

JSON | Python

Classification

Color model conversion

Color Model Conversion

Converts the color model of an image from the hue, saturation, and value (HSV) color space to red, green, and blue (RGB), or vice versa.

Python

Conversion

Colormap

Colormap

The Colormap function transforms the pixel values to display the raster data as a red, green, blue (RGB) color image, based on specific colors in a color map or a color range defined in a color ramp. For more information, see Colormap function.

JSON | Python

Conversion

Colormap to RGB

Colormap2RGB

Converts a single-band raster with a color map to a three-band (red, green, and blue) raster.

JSON | Python

Conversion

Complex

Complex

Computes magnitude from complex values.

JSON | Python

Conversion

Grayscale

Grayscale

Converts a multiband image into a single-band grayscale image. Specified weights can be applied to each of the input bands.

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Conversion

Rasterize attributes

RasterizeAttributes

The Rasterize Attributes function enriches a raster by adding bands derived from values of specified attributes, from an external table, or from a feature service.

Conversion

Rasterize features

RasterizeFeatures

Convert polygon, polyline and point feature class data to a raster layer.

Conversion

Remap

Remap

The Remap function allows you to change or reclassify the pixel values of the raster data. For more information, see Remap function.

JSON | Python

Conversion

Spectral conversion

SpectralConversion

The Spectral Conversion function applies a matrix to a multiband image to affect the color values of the output. This can be used, for example, to convert a false color infrared image to a pseudo natural color image.

JSON | Python

Conversion

Unit conversion

UnitConversion

The UnitConversion function performs unit conversions.

JSON | Python

Conversion

Vector field

VectorField

The VectorField function is used to composite two single-band rasters (each raster represents U/V or Magnitude/Direction) into a two-band raster (each band represents U/V or Magnitude/Direction). Data combination type (U-V or Magnitude-Direction) can also be converted interchangeably with this function.

JSON | Python

Conversion

Vector field renderer

VectorFieldRenderer

The VectorFieldRenderer function symbolizes a U-V or Magnitude-Direction raster.

JSON | Python

Conversion

Zonal remap

Zonal remap

This function allows you to remap pixels in a raster based on zones defined in another raster and zone-dependent value mapping defined in a table.

Conversion

Apparent reflectance

ApparentReflectance

This function calibrates the digital number (DN) values of imagery from some satellite sensors. The calibration uses sun elevation, acquisition date, sensor gain and bias for each band to derive Top of Atmosphere reflectance, plus sun angle correction.

Correction

Geometric

Geometric

The Geometric function transforms the image (for example, orthorectification) based on a sensor definition and a terrain model.

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Correction

Radar calibration

RadarCalibration

Calibration is performed on radar imagery so that the pixel values are a true representation of the radar backscatter.

Correction

Sentinel-1 Radiometric Calibration

Sentinel-1 RadiometricCalibration

Performs different types of radiometric calibration on Sentinel-1 data.

Correction

Sentinel-1 Thermal Noise Removal

Sentinel-1 Thermal Noise Removal

Removes thermal noise from Sentinel-1 data.

Correction

Speckle

Speckle

Filters the speckled radar dataset and smooths out the noise while retaining the edges or sharp features in the image.

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Correction

Attribute table

AttributeTable

Allows you to define an attribute table to symbolize a single-band mosaic dataset or raster dataset. This is useful when you want to present imagery that has discrete categories.

Data Management

Buffered

Buffered

The Buffered function is used to optimize the performance of complex function chains. It stores the output in memory of the part of the function chain which comes before it.

Data Management

Clip

Clip

Clips a raster using a rectangular shape according to the extents defined or will clip a raster to the shape of an input polygon feature class. The shape defining the clip can clip the extent of the raster or clip out an area within the raster.

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Data Management

Composite bands

CompositeBand

The CompositeBand function allows you to combine multiple images to form a multiband image.

JSON | Python

Data Management

Constant

Constant

Creates a virtual raster with a single pixel value that can be used in raster function templates and to process a mosaic dataset.

Data Management

Extract bands

ExtractBand

The ExtractBand function allows you to extract one or more bands from a raster, or it can reorder the bands in a multiband image.

JSON | Python

Data Management

Identity

Identity

This function is used to define the source raster as part of the default mosaicking behavior of the mosaic dataset. This function is a no-op function and takes no arguments except a raster.

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Data Management

Interpolate irregular data

Interpolate Irregular Data

Some netCDF or HDF datasets store their geolocation as irregularly spaced arrays of pixels or point data. When adding these datasets to a mosaic dataset, the interpolate irregular data function takes the irregularly gridded data and resamples it so each pixel is of uniform size and is square.

Data Management

Key metadata

KeyMetadata

This function allows you to insert or override key metadata of a raster.

Data Management

Mask

Mask

The Mask function changes the image by specifying a certain pixel value or a range of pixel values as no data.

JSON | Python

Data Management

Nibble

Nibble

Replaces cells of a raster corresponding to a mask with the values of the nearest neighbors.

Python

Data Management

Mosaic rasters

MosaicRasters

Creates a mosaic image out of multiple images.

Data Management

Raster information

RasterInfo

Modifies properties of the raster, such as bit depth, NoData value, and cell size.

Data Management

Recast

Recast

The Recast function reassigns argument values in an existing function template.

JSON

Data Management

Reproject

Reproject

The Reproject function modifies the projection of a raster dataset, mosaic dataset, or raster item in a mosaic dataset. It can also resample the data to a new cell size and define an origin.

Data Management

Resample

Resample

The Resample function resamples pixel values from a given resolution.

JSON | Python

Data Management

Swath

Some netCDF or HDF datasets store their geolocation as irregularly spaced arrays. When adding these datasets to a mosaic dataset, the swath function takes the irregularly gridded data and resamples it so that each pixel is of uniform size and is square.

Data Management

Transpose bits

TransposeBits

The TransposeBits function performs a bit operation. It extracts bit values from the source data and assigns them to new bits in the output data.

JSON | Python

Data Management

Cost allocation

Cost_Allocation

Calculates, for each cell, its least-cost source based on the least accumulative cost over a cost surface.

Python

Distance

Cost distance

Cost_Distance

Calculates the least accumulative cost distance for each cell from or to the least-cost source over a cost surface.

Python

Distance

Euclidean allocation

Euclidean_Allocation

Calculates, for each cell, the nearest source based on Euclidean distance.

Python

Distance

Euclidean distance

Euclidean_Distance

Calculates, for each cell, the direction, in degrees, to the nearest source.

Python

Distance

Least cost path

Least_Cost_Path

Calculates the least-cost path from a source to a destination.

Python

Distance

Fill

Fill

Fills sinks in a surface raster to remove small imperfections in the data.

Python

Hydrology

Flow accumulation

Flow_Accumulation

Creates a raster of accumulated flow into each cell. A weight factor can optionally be applied.

Python

Hydrology

Flow Distance

flow_distance

Computes the minimum downslope horizontal or vertical distance to cell(s) on a stream or river into which they flow, for each cell.

Python

Hydrology

Flow Direction

flow_direction

Replaces cells of a raster corresponding to a mask with the values of the nearest neighbors.

Python

Hydrology

Stream link

Stream_Link

Assigns unique values to sections of a raster linear network between intersections.

Python

Hydrology

Watershed

Watershed

Determines the contributing area above a set of cells in a raster.

Python

Hydrology

Absolute value

Abs

Calculates the absolute value of the pixels in a raster.

Python

Math

Arithmetic

Arithmetic

The Arithmetic function performs an arithmetic operation between two rasters or a raster and a scalar, and vice versa.

JSON | Python

Math

Band arithmetic

BandArithmetic

Calculates indexes using predefined formulas or a user-defined expression.

JSON | Python

Math

GEMI

BandArithmetic

The Global Environmental Monitoring Index (GEMI) is a nonlinear vegetation index for global environmental monitoring from satellite imagery. It's similar to NDVI, but it's less sensitive to atmospheric affects. It is affected by bare soil; therefore, it's not recommended for use in areas of sparse or moderately dense vegetation.

Python

Math

GVI

BandArithmetic

The Green Vegetation Index (GVI) was originally designed from Landsat MSS imagery and has been modified for Landsat TM imagery. It's also known as the Landsat TM Tasseled Cap green vegetation index. It could be used with imagery whose bands share the same spectral characteristics.

Python

Math

Modified SAVI

BandArithmetic

The Modified Soil Adjusted Vegetation Index (MSAVI2) tries to minimize the effect of bare soil on the SAVI.

Python

Math

NDVI

BandArithmetic

The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band.

Math

PVI

BandArithmetic

The Perpendicular Vegetation Index (PVI) is similar to a difference vegetation index; however, it is sensitive to atmospheric variations. When using this method to compare different images, it should only be used on images that have been atmospherically corrected.

Python

Math

SAVI

BandArithmetic

The Soil-Adjusted Vegetation Index (SAVI) is a vegetation index that attempts to minimize soil brightness influences using a soil-brightness correction factor. This is often used in arid regions where vegetative cover is low.

Python

Math

Sultan's formula

BandArithmetic

The Sultans process takes a six-band 8-bit image and uses the Sultan's formula to produce a three-band 8-bit image. The resulting image highlights rock formations called ophiolites on coastlines. This formula was designed based on the TM or ETM bands of a Landsat 5 or 7 scene. The equations applied to create each output band is as follows:

Band 1 = (Band5 / Band7) x 100
Band 2 = (Band5 / Band1) x 100
Band 3 = (Band3 / Band4) x (Band5 / Band4) x 100

Python

Math

Transformated SAVI

BandArithmetic

The Transformed Soil Adjusted Vegetation Index (TSAVI) is a vegetation index that attempts to minimize soil brightness influences by assuming the soil line has an arbitrary slope and intercept.

Python

Math

Calculator

RasterCalculator

Computes a raster from a raster based mathematical expression.

JSON | Python

Math

Divide

Local

Divides the values of two rasters on a pixel-by-pixel basis.

Python

Math

Exponent

Local

Calculates the base e exponential of the pixels in a raster.

Python

Math

Exp10

Local

Calculates the base 10 exponential of the pixels in a raster.

Python

Math

Exp2

Local

Calculates the base 2 exponential of the pixels in a raster.

Python

Math

Float

Local

Converts each pixel value of a raster into a floating-point representation.

Python

Math

Integer

Local

Converts each pixel value of a raster to an integer by truncation.

Python

Math

Ln

Local

Calculates the natural logarithm (base e) of each pixel in a raster.

Python

Math

Log10

Local

Calculates the base 10 logarithm of each pixel in a raster.

Python

Math

Log2

Local

Calculates the base 2 logarithm of each pixel in a raster.

Python

Math

Minus

Local

Subtracts the value of the second input raster from the value of the first input raster on a pixel-by-pixel basis.

Python

Math

Modulo

Local

Finds the remainder (modulo) of the first raster when divided by the second raster on a pixel-by-pixel basis.

Python

Math

Negate

Local

Changes the sign (multiplies by -1) of the pixel values of the input raster on a pixel-by-pixel basis.

Python

Math

Plus

Local

Adds (sums) the values of two rasters on a pixel-by-pixel basis.

Python

Math

Power

Local

Raises the pixel values in a raster to the power of the values found in another raster.

Python

Math

Round Down

Local

Returns the next lower integer, as a floating-point value, for each pixel in a raster.

Python

Math

Round Up

Local

Returns the next higher integer, as a floating-point value, for each pixel in a raster.

Python

Math

Square

Local

Calculates the square of the pixel values in a raster.

Python

Math

Square root

Local

Calculates the square root of the pixel values in a raster.

Python

Math

Times

Local

Multiplies the values of two rasters on a pixel-by-pixel basis.

Python

Math

Con

Local

Performs a conditional If, Then, Else operation. When a Con operator is used, there usually needs to be two or more functions chained together, where one function states the criteria and the second function is the Con operator which uses the criteria and dictates what the true and false outputs should be.

Python

Math: Conditional

Set Null

Local

Set Null sets identified cell locations to NoData based on a specified criteria. It returns NoData if a conditional evaluation is true, and returns the value specified by another raster if it is false.

Python

Math: Conditional

Bitwise And

Local

Performs a Bitwise And operation on the binary values of two input rasters.

Python

Math: Logical

Bitwise Left Shift

Local

Performs a Bitwise Left Shift operation on the binary values of two input rasters.

Python

Math: Logical

Bitwise Not

Local

Performs a Bitwise Not (complement) operation on the binary value of an input raster.

Python

Math: Logical

Bitwise Or

Local

Performs a Bitwise Or operation on the binary values of two input rasters.

Python

Math: Logical

Bitwise Right Shift

Local

Performs a Bitwise Right Shift operation on the binary values of two input rasters.

Python

Math: Logical

Bitwise Xor

Local

Performs a Bitwise eXclusive Or operation on the binary values of two input rasters.

Python

Math: Logical

Boolean And

Local

Performs a Boolean And operation on the pixel values of two input rasters.

If both input values are true (nonzero), the output value is 1. If one or both input values are false (zero), the output value is 0.

Python

Math: Logical

Boolean Not

Local

Performs a Boolean Not (complement) operation on the pixel values of the input raster.

Python

Math: Logical

Boolean Or

Local

Performs a Boolean Or operation on the cell values of two input rasters.

Python

Math: Logical

Boolean Xor

Local

Performs a Boolean eXclusive Or operation on the cell values of two input rasters.

Python

Math: Logical

Equal To

Local

Performs an equal-to operation on two rasters on a pixel-by-pixel basis.

Python

Math: Logical

Greater Than

Local

Performs a Relational greater-than operation on two inputs on a pixel-by-pixel basis.

Python

Math: Logical

Greater Than Equal

Local

Performs a Relational greater-than-or-equal-to operation on two inputs on a pixel-by-pixel basis.

Python

Math: Logical

Is Null

Local

Determines which values from the input raster are NoData on a pixel-by-pixel basis.

Python

Math: Logical

Less Than

Local

Performs a Relational less-than operation on two inputs on a pixel-by-pixel basis.

Python

Math: Logical

Less Than Equal

Local

Performs a Relational less-than-or-equal-to operation on two inputs on a pixel-by-pixel basis.

Python

Math: Logical

Not Equal

Local

Performs a Relational not-equal-to operation on two inputs on a pixel-by-pixel basis.

Python

Math: Logical

ACos

Local

Calculates the inverse cosine of the pixels in a raster.

Python

Math: Trigonometric

ACosH

Local

Calculates the inverse hyperbolic cosine of the pixels in a raster.

Python

Math: Trigonometric

ASin

Local

Calculates the inverse sine of the pixels in a raster.

Python

Math: Trigonometric

ASinH

Local

Calculates the inverse hyperbolic sine of the pixels in a raster.

Python

Math: Trigonometric

ATan

Local

Calculates the inverse tangent of the pixels in a raster.

Python

Math: Trigonometric

ATan2

Local

Calculates the inverse tangent (based on x,y) of the pixels in a raster.

Python

Math: Trigonometric

ATanH

Local

Calculates the inverse hyperbolic tangent of the pixels in a raster.

Python

Math: Trigonometric

Cos

Local

Calculates the cosine of the pixels in a raster.

Python

Math: Trigonometric

CosH

Local

Calculates the hyperbolic cosine of the pixels in a raster.

Python

Math: Trigonometric

Sin

Local

Calculates the sine of the pixels in a raster.

Python

Math: Trigonometric

SinH

Local

Calculates the hyperbolic sine of the pixels in a raster.

Python

Math: Trigonometric

Tan

Local

Calculates the tangent of the pixels in a raster.

Python

Math: Trigonometric

TanH

Local

Calculates the hyperbolic tangent of the pixels in a raster.

Python

Math: Trigonometric

ArgStatistics

ArgStatistics

The ArcStatistics function calculates arguments of the statistics. There are four ArcStatistics methods in this function: ArgMax, ArgMin, ArgMedian, and Duration.

Python

Statistical

Arg Max

ArgStatistics

ArgMax stands for the argument of the maximum. In the ArgMax method, all raster bands from every input raster are assigned a 0-based incremental band index.

Python

Statistical

Arg Median

ArgStatistics

The ArgMedian method returns the Band index for which the given pixel attains the median value of values from all bands.

Python

Statistical

Arg Min

ArgStatistics

ArgMin is the argument of the minimum, which returns the Band index for which the given pixel attains its minimum value.

Python

Statistical

Duration

ArgStatistics

The Duration method finds the longest consecutive elements in the array, where each element has a value greater than or equal to minimum and less than or equal to maximum, and then returns its length.

Python

Statistical

Cell statistics

CellStatistics

This function calculates statistics from multiple rasters, on a pixel-by-pixel basis. The available statistics are majority, maximum, mean, median, minimum, minority, range, standard deviation, sum, and variety.

Statistical

Majority cell statistics

CellStatistics

Determines the value that occurs most often on a pixel-by-pixel basis.

Python

Statistical

Maximum cell statistics

Cell Statistics

Determines the largest value on a pixel-by-pixel basis.

Python

Statistical

Mean cell statistics

Cell Statistics

Calculates the average on a pixel-by-pixel basis.

Python

Statistical

Median cell statistics

Cell Statistics

Calculates the middle value of the pixels on a pixel-by-pixel basis.

Python

Statistical

Minimum cell statistics

Cell Statistics

Determines the smallest value on a pixel-by-pixel basis.

Python

Statistical

Minority cell statistics

Cell Statistics

Determines the value that occurs least often on a pixel-by-pixel basis.

Python

Statistical

Range cell statistics

Cell Statistics

Calculates the difference between the largest and the smallest value on a pixel-by-pixel basis.

Python

Statistical

Standard Deviation cell statistics

Cell Statistics

Calculates the standard deviation of the pixels on a pixel-by-pixel basis.

Python

Statistical

Sum cell statistics

Cell Statistics

Calculates the total value on a pixel-by-pixel basis.

Python

Statistical

Variety cell statistics

Cell Statistics

Calculates the number of unique values on a pixel-by-pixel basis.

Python

Statistical

Statistics

Statistics

The Statistics function calculates focal statistics for each pixel of an image, based on a defined focal neighborhood.

JSON | Python

Statistical

Zonal statistics

ZonalStatistics

Calculates statistics on values of a raster within the zones of another dataset.

Python

Statistical

Aspect

Aspect

Aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. Aspect can be thought of as the slope direction. The values of the output raster will be the compass direction of the aspect.

JSON | Python

Surface

Contour

Contour

The Contour function generates contour lines by joining points with the same elevation from a raster elevation dataset. The contours are isolines created as rasters for visualization.

Surface

Curvature

Curvature

The Curvature function displays the shape or curvature of the slope. A part of a surface can be concave or convex; you can tell that by looking at the curvature value. The curvature is calculated by computing the second derivative of the surface.

JSON | Python

Surface

Elevation void fill

ElevationVoidFill

The Elevation Void Fill function is used to create pixels where holes exist in your elevation.

JSON | Python

Surface

Hillshade

Hillshade

The hillshade function produces a grayscale 3D representation of the terrain surface, with the sun's relative position taken into account for shading the image.

JSON | Python

Surface

Shaded relief

ShadedRelief

A color 3D representation of the terrain is created by merging the images from the elevation-coded and hillshade methods. This function uses the altitude and azimuth properties to specify the sun's position.

JSON | Python

Surface

Slope

Slope

Slope represents the rate of change of elevation for each digital elevation model (DEM) cell. It's the first derivative of a DEM.

JSON | Python

Surface

Viewshed

Viewshed

Determines the raster surface locations visible to a set of observer features using geodesic methods

Surface