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Raster loader

Note

Sedona loader are available in Scala, Java and Python and have the same APIs.

Load any raster to Raster format

The raster loader of Sedona leverages Spark built-in binary data source and works with several RS constructors to produce Raster type. Each raster is a row in the resulting DataFrame and stored in a Raster format.

By default, these functions uses lon/lat order since v1.5.0. Before, it used lat/lon order.

Load raster to a binary DataFrame

You can load any type of raster data using the code below. Then use the RS constructors below to create a Raster DataFrame.

sedona.read.format("binaryFile").load("/some/path/*.asc")

RS_FromArcInfoAsciiGrid

Introduction: Returns a raster geometry from an Arc Info Ascii Grid file.

Format: RS_FromArcInfoAsciiGrid(asc: ARRAY[Byte])

Since: v1.4.0

Spark SQL Example:

var df = sedona.read.format("binaryFile").load("/some/path/*.asc")
df = df.withColumn("raster", f.expr("RS_FromArcInfoAsciiGrid(content)"))

RS_FromGeoTiff

Introduction: Returns a raster geometry from a GeoTiff file.

Format: RS_FromGeoTiff(asc: ARRAY[Byte])

Since: v1.4.0

Spark SQL Example:

var df = sedona.read.format("binaryFile").load("/some/path/*.tiff")
df = df.withColumn("raster", f.expr("RS_FromGeoTiff(content)"))

RS_MakeEmptyRaster

Introduction: Returns an empty raster geometry. Every band in the raster is initialized to 0.0.

Since: v1.5.0

Format:

RS_MakeEmptyRaster(numBands: Integer, bandDataType: String = 'D', width: Integer, height: Integer, upperleftX: Double, upperleftY: Double, cellSize: Double)
  • NumBands: The number of bands in the raster. If not specified, the raster will have a single band.
  • BandDataType: Optional parameter specifying the data types of all the bands in the created raster. Accepts one of:
    1. "D" - 64 bits Double
    2. "F" - 32 bits Float
    3. "I" - 32 bits signed Integer
    4. "S" - 16 bits signed Short
    5. "US" - 16 bits unsigned Short
    6. "B" - 8 bits Byte
  • Width: The width of the raster in pixels.
  • Height: The height of the raster in pixels.
  • UpperleftX: The X coordinate of the upper left corner of the raster, in terms of the CRS units.
  • UpperleftY: The Y coordinate of the upper left corner of the raster, in terms of the CRS units.
  • Cell Size (pixel size): The size of the cells in the raster, in terms of the CRS units.

It uses the default Cartesian coordinate system.

Format:

RS_MakeEmptyRaster(numBands: Integer, bandDataType: String = 'D', width: Integer, height: Integer, upperleftX: Double, upperleftY: Double, scaleX: Double, scaleY: Double, skewX: Double, skewY: Double, srid: Integer)
  • NumBands: The number of bands in the raster. If not specified, the raster will have a single band.
  • BandDataType: Optional parameter specifying the data types of all the bands in the created raster. Accepts one of:
    1. "D" - 64 bits Double
    2. "F" - 32 bits Float
    3. "I" - 32 bits signed Integer
    4. "S" - 16 bits signed Short
    5. "US" - 16 bits unsigned Short
    6. "B" - 8 bits Byte
  • Width: The width of the raster in pixels.
  • Height: The height of the raster in pixels.
  • UpperleftX: The X coordinate of the upper left corner of the raster, in terms of the CRS units.
  • UpperleftY: The Y coordinate of the upper left corner of the raster, in terms of the CRS units.
  • ScaleX (pixel size on X): The size of the cells on the X axis, in terms of the CRS units.
  • ScaleY (pixel size on Y): The size of the cells on the Y axis, in terms of the CRS units.
  • SkewX: The skew of the raster on the X axis, in terms of the CRS units.
  • SkewY: The skew of the raster on the Y axis, in terms of the CRS units.
  • SRID: The SRID of the raster. Use 0 if you want to use the default Cartesian coordinate system. Use 4326 if you want to use WGS84.

Note

If any other value than the accepted values for the bandDataType is provided, RS_MakeEmptyRaster defaults to double as the data type for the raster.

Spark SQL example 1 (with 2 bands):

SELECT RS_MakeEmptyRaster(2, 10, 10, 0.0, 0.0, 1.0)

Output:

+--------------------------------------------+
|rs_makeemptyraster(2, 10, 10, 0.0, 0.0, 1.0)|
+--------------------------------------------+
|                        GridCoverage2D["g...|
+--------------------------------------------+

Spark SQL example 2 (with 2 bands and dataType):

SELECT RS_MakeEmptyRaster(2, 'I', 10, 10, 0.0, 0.0, 1.0) - Create a raster with integer datatype

Output:

+--------------------------------------------+
|rs_makeemptyraster(2, 10, 10, 0.0, 0.0, 1.0)|
+--------------------------------------------+
|                        GridCoverage2D["g...|
+--------------------------------------------+

Spark SQL example 3 (with 2 bands, scale, skew, and SRID):

SELECT RS_MakeEmptyRaster(2, 10, 10, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 4326)

Output:

+------------------------------------------------------------------+
|rs_makeemptyraster(2, 10, 10, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 4326)|
+------------------------------------------------------------------+
|                                              GridCoverage2D["g...|
+------------------------------------------------------------------+

Spark SQL example 4 (with 2 bands, scale, skew, and SRID):

SELECT RS_MakeEmptyRaster(2, 'F', 10, 10, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 4326) - Create a raster with float datatype

Output:

+------------------------------------------------------------------+
|rs_makeemptyraster(2, 10, 10, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 4326)|
+------------------------------------------------------------------+
|                                              GridCoverage2D["g...|
+------------------------------------------------------------------+

RS_FromNetCDF

Introduction: Returns a raster geometry representing the given record variable short name from a NetCDF file. This API reads the array data of the record variable in memory along with all its dimensions Since the netCDF format has many variants, the reader might not work for your test case, if that is so, please report this using the public forums.

This API has been tested for netCDF classic (NetCDF 1, 2, 5) and netCDF4/HDF5 files.

This API requires the name of the record variable. It is assumed that a variable of the given name exists, and its last 2 dimensions are 'lat' and 'lon' dimensions respectively.

If this assumption does not hold true for your case, you can choose to pass the lonDimensionName and latDimensionName explicitly.

You can use RS_NetCDFInfo to get the details of the passed netCDF file (variables and its dimensions).

Format 1: RS_FromNetCDF(netCDF: ARRAY[Byte], recordVariableName: String)

Format 2: RS_FromNetCDF(netCDF: ARRAY[Byte], recordVariableName: String, lonDimensionName: String, latDimensionName: String)

Since: v1.5.1

Spark Example:

val df = sedona.read.format("binaryFile").load("/some/path/test.nc")
df = df.withColumn("raster", f.expr("RS_FromNetCDF(content, 'O3')"))
val df = sedona.read.format("binaryFile").load("/some/path/test.nc")
df = df.withColumn("raster", f.expr("RS_FromNetCDF(content, 'O3', 'lon', 'lat')"))

RS_NetCDFInfo

Introduction: Returns a string containing names of the variables in a given netCDF file along with its dimensions.

Format: RS_NetCDFInfo(netCDF: ARRAY[Byte])

Since: 1.5.1

Spark Example:

val df = sedona.read.format("binaryFile").load("/some/path/test.nc")
recordInfo = df.selectExpr("RS_NetCDFInfo(content) as record_info").first().getString(0)
print(recordInfo)

Output:

O3(time=2, z=2, lat=48, lon=80)

NO2(time=2, z=2, lat=48, lon=80)

Last update: January 6, 2024 22:01:24