GeoWave Quickstart Guide Raster Demo

In the Raster Demo, the Geowave Service creates a mosaic image from a set of raster images. The analyze commands filter the set images to select ones that meet our given parameters, and the ingest command downloads those images and combines them to create a mosaic.

Set-Up Environment Variables

Download and source the GeoWave environment script.

If you have already done this in the Vector Demo, you can skip this step.

This script defines a number of the variables that will be used in future commands.

If running with the GeoWave Sandbox:

cd /mnt

sudo wget s3.amazonaws.com/geowave/latest/scripts/sandbox/quickstart/geowave-env.sh

source /mnt/geowave-env.sh

If running with EMR:

cd /mnt

sudo wget s3.amazonaws.com/geowave/latest/scripts/emr/quickstart/geowave-env.sh

source /mnt/geowave-env.sh

Download and Unpack the GDAL Library

GDAL (Geospatial Data Abstraction Library) is an image processing library. We need to download the proper GDAL files to process the raster data. More info on GDAL can be found here.

sudo mkdir /mnt/gdal

cd /mnt/gdal

sudo wget http://demo.geo-solutions.it/share/github/imageio-ext/releases/1.1.X/1.1.7/native/gdal/linux/gdal192-CentOS5.8-gcc4.1.2-x86_64.tar.gz

sudo tar -xvf gdal192-CentOS5.8-gcc4.1.2-x86_64.tar.gz

Set the path to the GDAL libraries.

export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/mnt/gdal

At this point, we should be ready to ingest the data.

Config and Ingest.

Add a GeoWave store

We need to create two GeoWave stores for this ingest. landsat_raster will store the raster images, and landsat_vector stores the vector metadata associated with those images. The images come from the Landsat 8 Satellite.

If running with the GeoWave Sandbox:

geowave config addstore -t hbase -z $HOSTNAME:2181 landsatraster --gwNamespace landsat_raster

geowave config cpstore landsatraster landsatvector --gwNamespace landsat_vector

If running with EMR Accumulo:

geowave config addstore -t accumulo -z $HOSTNAME:2181 landsatraster --gwNamespace geowave.landsat_raster \
-i accumulo -u geowave -p geowave

geowave config cpstore landsatraster landsatvector --gwNamespace geowave.landsat_vector

If running with EMR HBase

geowave config addstore -t hbase -z $HOSTNAME:2181 landsatraster --gwNamespace geowave.landsat_raster

geowave config cpstore landsatraster landsatvector --gwNamespace geowave.landsat_vector

If running with EMR Cassandra

geowave config addstore -t cassandra --contactPoints localhost landsatraster --gwNamespace geowave.landsat_raster --batchWriteSize 15

geowave config cpstore landsatraster landsatvector --gwNamespace geowave.landsat_vector

Add a spatial index.

We’re going to project this data over the Web Mercator Coordinate Reference System (CRS) to demonstrate GeoWave’s Configurable Indexed CRS capabilities.
```
geowave config addindex -t spatial spatial -c EPSG:3857
```

Filter the dataset by calling analyze.

In this example we look for Band 8 images that are in Berlin.

geowave landsat analyze --nbestperspatial --nbestscenes 1 --usecachedscenes --cql  "INTERSECTS(shape,$GERMANY) AND band='B8' AND cloudCover>0" -ws /mnt/landsat

geowave landsat analyze --nbestperspatial --nbestscenes 1 --usecachedscenes --cql "$BERLIN_BBOX AND band='B8' AND cloudCover>0" -ws /mnt/landsat

Ingest the data into geowave

Call the ingest command to download the data, ingest it into the GeoWave stores, and create the mosaic image. Status can be monitored at using HBase or Accumulo web interface. See the Interacting with the cluster section help doing so. More information on the landsat ingest and analyze commands can be found here.
```
geowave landsat ingest --nbestperspatial --nbestscenes 1 --usecachedscenes --cql "$BERLIN_BBOX AND band='B8' AND cloudCover>0" --crop --retainimages -ws /mnt/landsat --vectorstore landsatvector --pyramid --coverage berlin_mosaic  landsatraster spatial
```