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:

R package for time-series animation of rasters

R package for time-series animation of rasters

One of remote sensing's greatest strengths is its capability to deliver consistent and highly resolved time series. These have a wonderful potential for visualizations and analysis.Creating a fluid and pretty animation from a collection of tifs can be complicated, as...

WASCAL MSc Program course on spatial data analysis

WASCAL MSc Program course on spatial data analysis

As part of the project WASCAL-DE-Coop, the Department of Remote Sensing at the University of Würzburg conducted the second course on Spatial Data Analysis, embedded in the WASCAL Master Research Program "Climate Change and Informatics" at the University Université...

most recent news:

R package for time-series animation of rasters

R package for time-series animation of rasters

One of remote sensing's greatest strengths is its capability to deliver consistent and highly resolved time series. These have a wonderful potential for visualizations and analysis.Creating a fluid and pretty animation from a collection of tifs can be complicated, as...

Who can live without chocolates?

Who can live without chocolates?

Itohan-Osa Abu and Dr Michael Thiel, have published a paper in Ecological Indicators in cooperation with the partners at the Joint Research Centre (JRC) titled "Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas". From the...

Our Virtual teaching setup

Our Virtual teaching setup

We posted a while ago some images about our virtual teaching room and mentioned that we will provide briefly the general technical background setup: we have four lecture rooms each with a broadcast camera (that can stream for 6 hours and more), microphones and green...