This project demonstrates generating polygonal summary for a feature data set over high resolution raster.

Features used are building footprints from: https://github.com/Microsoft/USBuildingFootprints

This dataset contains 125,192,184 computer generated building footprints in all 50 US states.

These features are read directly from zipped GeoJSON files.

The rasters used are 1/3 arc-second NED GeoTiffs hosted at: s3://azavea-datahub/raw/ned-13arcsec-geotiff/ The files are gridded and named by their northing and easting degree (ex: imgn36w092_13.tif).

The rasters are sampled directly from that location.

Output is a vector tile layer that includes the min and max elevation under each geometry.

build.sbt: Scala Build Tool build configuration file .sbtopts: Command line options for SBT, including JVM parameters project: Additional configuration for SBT project, plugins, utility, versions src/main/scala: Application and utility code

sbt:geotrellis-usbuildings> test:runMain usbuildings.Main --buildings https://usbuildingdata.blob.core.windows.net/usbuildings-v1-1/RhodeIsland.zip --output file:/tmp/usbuildings-ri-v01

At minimum, you'll need to change the following sbt lighter configuration variables in build.sbt to resources within your AWS account before starting the EMR cluster:

• sparkS3JarFolder: Must be an S3 path within the AWS account that the cluster can read/write to
• sparkS3LogUri: Must be an S3 path within the AWS account that the cluster can read/write to
• sparkJobFlowInstancesConfig: Must point to the EC2 key name you have access to

Be sure to reload in sbt after making changes to build.sbt.

When running the commands below, be sure you replace the S3 url for the --output parameter to point to a valid S3 path that the EMR cluster has permissions to write to.

sbt:geotrellis-usbuildings> sparkSubmit --all-buildings --buildings foo --output s3://bucket/path/prefix