RS_Values

Introduction: Returns the values at the given points or grid coordinates in the raster. If no band number is specified it defaults to 1.

RS_Values is similar to RS_Value but operates on an array of points or grid coordinates. RS_Values can be significantly faster since a raster only has to be loaded once for several points.

Note

Since v1.5.1, if the coordinate reference system (CRS) of the input points geometries differs from that of the raster, then points will be transformed to match the CRS of the raster. If the raster or points doesn't have a CRS then it will default to 4326/WGS84.

Format:

RS_Values (raster: Raster, points: ARRAY[Geometry])

RS_Values (raster: Raster, points: ARRAY[Geometry], band: Integer)

RS_Values (raster: Raster, xCoordinates: ARRAY[Integer], yCoordinates: ARRAY[Integer], band: Integer)

Since: v1.4.0

SQL Example

• For Array of Point geometries:

SELECT RS_Values(raster, Array(ST_Point(-1307.5, 400.8), ST_Point(-1403.3, 399.1)))
FROM raster_table

• For Arrays of grid coordinates:

SELECT RS_Values(raster, Array(4, 5), Array(3, 2), 1) FROM raster_table

Output:

Array(5.0, 3.0)

Spark SQL example for joining a point dataset with a raster dataset:

val pointDf = sedona.read...
val rasterDf = sedona.read.format("binaryFile").load("/some/path/*.tiff")
  .withColumn("raster", expr("RS_FromGeoTiff(content)"))
  .withColumn("envelope", expr("RS_Envelope(raster)"))

// Join the points with the raster extent and aggregate points to arrays.
// We only use the path and envelope of the raster to keep the shuffle as small as possible.
val df = pointDf.join(rasterDf.select("path", "envelope"), expr("ST_Within(point_geom, envelope)"))
  .groupBy("path")
  .agg(collect_list("point_geom").alias("point"), collect_list("point_id").alias("id"))

df.join(rasterDf, "path")
  .selectExpr("explode(arrays_zip(id, point, RS_Values(raster, point))) as result")
  .selectExpr("result.*")
  .show()

Output:

+----+------------+-------+
| id | point      | value |
+----+------------+-------+
|  4 | POINT(1 1) |   3.0 |
|  5 | POINT(2 2) |   7.0 |
+----+------------+-------+