Managing raster data with PostGIS and Python

Managing raster data with PostGIS and Python

February 3, 2016

PostGIS is the spatial extension of the open source database management system PostgreSQL. It helps you to manage your data (vector and raster) within a coherent geodatabase through a variety of spatial functions. Having a spatial database, the times of data clutter and messiness are over, especially when you are dealing with big data. Initially PostGIS was created to for the handling of vector data only. However, during the recent years more and more raster handling functionalities were introduced. For a complete overview of spatial raster operators, have a look at: http://postgis.net/docs/manual-2.1/RT_reference.html

 

Download and install PostgreSQL and PostGIS

Download PostgreSQL from here: http://www.postgresql.org/download/

The installer for PostgreSQL includes the PostgreSQL server, pgAdmin III; a GUI for managing and developing your databases, and StackBuilder; a package manager that can be used to download and install additional PostgreSQL applications and drivers. From the StackBuilder GUI, select Spatial Extensions and install the respective PostGIS 2.2 Bundle for PostgreSQL.

 

Create a new spatial database

In pgAdmin, create a new database (right click: New Database):

db

and the spatial extension postgis (right click on Extensions: New Extension):

pg

This will create a first table within your DB: spatial_ref_sys that contains the coordinate systems, map projections and the spatial indeces.

 

Set up Python

Python provides awesome functionality for the automated raster processing within PostGIS. Automatization is necessary especially when you deal with a lot of data and iterative processes. Python scripting is also needed as the pgAdmin GUI does not support the access of all functions.

Download Python 2.7 from here: https://www.python.org/downloads/

Psycopg2 is a Python library that accesses the objects of the PostgreSQL server and allows the execution of PostGIS commands from Python.

Download psycopg2 from here: http://www.stickpeople.com/projects/python/win-psycopg/2.6.1/psycopg2-2.6.1.win32-py2.7-pg9.4.4-release.exe

 

PostGIS scripting with Python

Import the Python libraries:

import psycopg2  
import subprocess 
import sys, os

Set up input path and a loop that goes through all TIFs in the directory:

input_path = "C:\\Data\\Raster\\"

for raster in os.listdir(input_path):    
    if raster.endswith(".tif"):
       name = raster.split(".tif")[0]
       raster = os.path.join(input_path, raster)

Connect to the PostgreSQL server:

       os.environ['PATH'] = r';C:\Program Files\PostgreSQL\9.4\bin'
       os.environ['PGHOST'] = 'localhost'
       os.environ['PGPORT'] = '5432'
       os.environ['PGUSER'] = 'postgres'
       os.environ['PGPASSWORD'] = 'postgres'
       os.environ['PGDATABASE'] = 'raster_database'
     
       rastername = str(name)
       rasterlayer = rastername.lower()
  
       conn = psycopg2.connect(database="raster_database", user="postgres", host="localhost", password="postgres") 
       cursor = conn.cursor()

Import each raster through raster2pgsql function (coordinate system epsg code is set to 32633 UTM):

       cmds = 'raster2pgsql -s 32633 -t 2000x2000 "' + raster + '" |psql'
       subprocess.call(cmds, shell=True)

Now run any PostGIS command you like. In this example we run rescale the raster to 250m spatial resolution and reproject it from UTM 33N to WGS84 (epsg code 4326). At the end, the raster may be exported locally to *.hex data format. The export is optional, we could also convert the raster to CSV or numpy array.

       sql = "UPDATE " + rasterlayer + " SET rast = ST_Rescale(rast, 250, 'Near'); \
              UPDATE " + rasterlayer + " SET rast = ST_Transform(ST_SetSRID(rast,32633),4326);"
       cursor.execute(sql)
       conn.commit()

       rql = "COPY (SELECT encode(ST_AsTIFF(rast), 'hex') AS tif FROM " + rasterlayer + ") TO 'C:/Users/Data/" + rasterlayer + ".hex';"
       cursor.execute(rql)
       conn.commit()

 

you may also like:

Book “Intro to Spatial Data Analysis” in Print

Book “Intro to Spatial Data Analysis” in Print

Our upcoming book "Introduction to Spatial Data Analysis" is finally in print and delivery is expected around end of July. This book provides a gentle introduction to spatial data analysis using mainly QGIS and also working on the command line (R) with spatial data....

R Package for harmonic modelling of time-series data

R Package for harmonic modelling of time-series data

Sentinel-2 NDVI time-series over the Steigerwald. Left: Original satellite scenes after cloud, cloud shadow and snow maksing. Right: Interpolated time-series using a harmonic modelling. In order to fully exploit the monitoring potential of the satellite systems,...

most recent news:

Field work equipment arrived with Earth Observation design

Field work equipment arrived with Earth Observation design

Our field work jackets and backpacks arrived with Earth Observation design. Now we can conduct our UAV campaigns and are easily recognizable as researcher of the University of Wuerzburg. We are looking forward to some time in the field sampling various landcover...

New researcher Pawel Kluter

New researcher Pawel Kluter

Pawel Kluter joined the Department of Remote Sensing as a Research Associate in November 2020. His main role is the deployment of Data Cubes in cloud environments (Front End / Back End), as well as the development of remote sensing processing routines using Python....