Convert Easting Northing Coordinates to Latitude and Longitude in Scala/Spark with EPSG Coordinate Transformation

I’m working on a project in Scala and Apache Spark where I need to convert coordinates from Easting/Northing (EPSG:27700) to Latitude/Longitude (EPSG:4326). I have a Python script that uses in built libraries pyproj (transformer) to handle this, but I haven’t found an equivalent way to do it in Scala/Spark.

Here’s the Python code I’m currently using:

    from pyproj import Transformer
    import pandas as pd

    data = {
        'node_id': ['94489', '94555', '94806', '99118', '76202'],
        'easting': [276164, 428790, 357501, 439545, 357353],
        'northing': [84185, 92790, 173246, 336877, 170708]
    }

    df = pd.DataFrame(data)
    transformer = Transformer.from_crs("epsg:27700", "epsg:4326")
    lat, lon = transformer.transform(df['easting'].values, df['northing'].values)
    df['longitude'] = lon
    df['latitude'] = lat
    print(df)

The output DataFrame should looks like this:

@MartinHH Thanks for providing the reference. Looks like it is possible to create same code in scala/spark

package com.test.job.function_testing

import org.apache.spark.sql.SparkSession
import geotrellis.proj4.CRS
import geotrellis.proj4.Transform

object TestCode {

  def main(args: Array[String]) = {

    val runLocally = true
    val jobName = "Test Spark Logging Case"

    implicit val spark: SparkSession = Some(SparkSession.builder.appName(jobName))
      .map(sparkSessionBuilder =>
        if (runLocally) sparkSessionBuilder.master("local[2]") else sparkSessionBuilder
      )
      .map(_.getOrCreate())
      .get

    import spark.implicits._
    val columns = Seq("node_id", "easting", "northing")
    val data = Seq(
      (94489, 276164, 84185),
      (94555, 428790, 92790),
      (94806, 357501, 173246),
      (99118, 439545, 336877),
      (76202, 357353, 170708))

    val df = data.toDF(columns: _*)

    val eastingNorthing = CRS.fromEpsgCode(27700)
    val latLong = CRS.fromEpsgCode(4326)
    val transform = Transform(eastingNorthing, latLong)
    import org.apache.spark.sql.functions._

    def transformlatlong = udf((easting: Int, northing: Int) => {
      val (long, lat) = transform(easting, northing)
      (long,lat)
    }
    )

    val newdf = df.withColumn("latlong",transformlatlong(df("easting"),df("northing")))
    newdf.select(col("node_id"),col("easting"),col("northing"),col("latlong._1").as("longitude"),col("latlong._2").as("latitude")).show()

  }

}

And here is the output dataframe

+-------+-------+--------+-------------------+------------------+
|node_id|easting|northing|          longitude|          latitude|
+-------+-------+--------+-------------------+------------------+
|  94489| 276164|   84185| -3.752810925839862|50.644154475886154|
|  94555| 428790|   92790|-1.5934125598396651| 50.73401609723385|
|  94806| 357501|  173246|-2.6130593045676984| 51.45658738605824|
|  99118| 439545|  336877| -1.413187622652739| 52.92785156624134|
|  76202| 357353|  170708| -2.614882589162872| 51.43375699275326|
+-------+-------+--------+-------------------+------------------+

And added below library to the build.sbt

libraryDependencies += "org.locationtech.geotrellis" %% "geotrellis-raster" % "3.5.2"