Chapter 11. Raster Data Management, Queries, and Applications

Chapter 11. Raster Data Management, Queries, and Applications

11.1. Loading and Creating Rasters

For most use cases, you will create PostGIS rasters by loading existing raster files using the packaged raster2pgsql raster loader.

11.1.1. Using raster2pgsql to load rasters

The raster2pgsql is a raster loader executable that loads GDAL supported raster formats into sql suitable for loading into a PostGIS raster table. It is capable of loading folders of raster files as well as creating overviews of rasters.

Since the raster2pgsql is compiled as part of PostGIS most often (unless you compile your own GDAL library), the raster types supported by the executable will be the same as those compiled in the GDAL dependency library. To get a list of raster types your particular raster2pgsql supports use the -G switch. These should be the same as those provided by your PostGIS install documented here ST_GDALDrivers if you are using the same GDAL library for both.


The older version of this tool was a python script. The executable has replaced the python script. If you still find the need for the Python script Examples of the python one can be found at GDAL PostGIS Raster Driver Usage . Please note that the raster2pgsql python script may not work with future versions of PostGIS raster and is no longer supported.


When creating overviews of a specific factor from a set of rasters that are aligned, it is possible for the overviews to not align. Visit for an example where the overviews do not align.


raster2pgsql raster_options_go_here raster_file someschema.sometable > out.sql


Display help screen. Help will also display if you don't pass in any arguments.


Print the supported raster formats.

(c|a|d|p) These are mutually exclusive options:


Create new table and populate it with raster(s), this is the default mode


Append raster(s) to an existing table.


Drop table, create new one and populate it with raster(s)


Prepare mode, only create the table.

Raster processing: Applying constraints for proper registering in raster catalogs


Apply raster constraints -- srid, pixelsize etc. to ensure raster is properly registered in raster_columns view.


Disable setting the max extent constraint. Only applied if -C flag is also used.


Set the constraints (spatially unique and coverage tile) for regular blocking. Only applied if -C flag is also used.

Raster processing: Optional parameters used to manipulate input raster dataset

-s <SRID>

Assign output raster with specified SRID. If not provided or is zero, raster's metadata will be checked to determine an appropriate SRID.


Index (1-based) of band to extract from raster. For more than one band index, separate with comma (,). If unspecified, all bands of raster will be extracted.


Cut raster into tiles to be inserted one per table row. TILE_SIZE is expressed as WIDTHxHEIGHT or set to the value "auto" to allow the loader to compute an appropriate tile size using the first raster and applied to all rasters.


Pad right-most and bottom-most tiles to guarantee that all tiles have the same width and height.

-R, --register

Register the raster as a filesystem (out-db) raster.

Only the metadata of the raster and path location to the raster is stored in the database (not the pixels).


Create overview of the raster. For more than one factor, separate with comma(,). Overview table name follows the pattern o_ overview factor _ table , where overview factor is a placeholder for numerical overview factor and table is replaced with the base table name. Created overview is stored in the database and is not affected by -R. Note that your generated sql file will contain both the main table and overview tables.


NODATA value to use on bands without a NODATA value.

Optional parameters used to manipulate database objects


Specify name of destination raster column, default is 'rast'


Add a column with the name of the file


Specify the name of the filename column. Implies -F.


Wrap PostgreSQL identifiers in quotes.


Create a GiST index on the raster column.


Vacuum analyze the raster table.


Skip NODATA value checks for each raster band.

-T tablespace

Specify the tablespace for the new table. Note that indices (including the primary key) will still use the default tablespace unless the -X flag is also used.

-X tablespace

Specify the tablespace for the table's new index. This applies to the primary key and the spatial index if the -I flag is used.


Use copy statements instead of insert statements.


Execute each statement individually, do not use a transaction.


Control endianness of generated binary output of raster; specify 0 for XDR and 1 for NDR (default); only NDR output is supported now

-V version

Specify version of output format. Default is 0. Only 0 is supported at this time.

An example session using the loader to create an input file and uploading it chunked in 100x100 tiles might look like this:


You can leave the schema name out e.g demelevation instead of public.demelevation and the raster table will be created in the default schema of the database or user

raster2pgsql -s 4326 -I -C -M *.tif -F -t 100x100 public.demelevation > elev.sql
psql -d gisdb -f elev.sql

A conversion and upload can be done all in one step using UNIX pipes:

raster2pgsql -s 4326 -I -C -M *.tif -F -t 100x100 public.demelevation | psql -d gisdb

Load rasters Massachusetts state plane meters aerial tiles into a schema called aerial and create a full view, 2 and 4 level overview tables, use copy mode for inserting (no intermediary file just straight to db), and -e don't force everything in a transaction (good if you want to see data in tables right away without waiting). Break up the rasters into 128x128 pixel tiles and apply raster constraints. Use copy mode instead of table insert. (-F) Include a field called filename to hold the name of the file the tiles were cut from.

raster2pgsql -I -C -e -Y -F -s 26986 -t 128x128  -l 2,4 bostonaerials2008/*.jpg | psql -U postgres -d gisdb -h localhost -p 5432
--get a list of raster types supported:
raster2pgsql -G

The -G commands outputs a list something like

Available GDAL raster formats:
  Virtual Raster
  National Imagery Transmission Format
  Raster Product Format TOC format
  ECRG TOC format
  Erdas Imagine Images (.img)
  CEOS SAR Image
  CEOS Image
  JAXA PALSAR Product Reader (Level 1.1/1.5)
  Ground-based SAR Applications Testbed File Format (.gff)
  Arc/Info Binary Grid
  Arc/Info ASCII Grid
  SDTS Raster
  DTED Elevation Raster
  Portable Network Graphics
  In Memory Raster
  Japanese DEM (.mem)
  Graphics Interchange Format (.gif)
  Graphics Interchange Format (.gif)
  Envisat Image Format
  Maptech BSB Nautical Charts
  X11 PixMap Format
  MS Windows Device Independent Bitmap
  AirSAR Polarimetric Image
  RadarSat 2 XML Product
  PCIDSK Database File
  PCRaster Raster File
  ILWIS Raster Map
  SGI Image File Format 1.0
  SRTMHGT File Format
  Leveller heightfield
  Terragen heightfield
  USGS Astrogeology ISIS cube (Version 3)
  USGS Astrogeology ISIS cube (Version 2)
  NASA Planetary Data System
  EarthWatch .TIL
  ERMapper .ers Labelled
  NOAA Polar Orbiter Level 1b Data Set
  FIT Image
  GRIdded Binary (.grb)
  Raster Matrix Format
  EUMETSAT Archive native (.nat)
  Idrisi Raster A.1
  Intergraph Raster
  Golden Software ASCII Grid (.grd)
  Golden Software Binary Grid (.grd)
  Golden Software 7 Binary Grid (.grd)
  COSAR Annotated Binary Matrix (TerraSAR-X)
  TerraSAR-X Product
  DRDC COASP SAR Processor Raster
  R Object Data Store
  Portable Pixmap Format (netpbm)
  USGS DOQ (Old Style)
  USGS DOQ (New Style)
  ENVI .hdr Labelled
  ESRI .hdr Labelled
  Generic Binary (.hdr Labelled)
  PCI .aux Labelled
  Vexcel MFF Raster
  Vexcel MFF2 (HKV) Raster
  Fuji BAS Scanner Image
  GSC Geogrid
  VTP .bt (Binary Terrain) 1.3 Format
  Erdas .LAN/.GIS
  Convair PolGASP
  Image Data and Analysis
  NLAPS Data Format
  Erdas Imagine Raw
  FARSITE v.4 Landscape File (.lcp)
  NOAA Vertical Datum .GTX
  NADCON .los/.las Datum Grid Shift
  NTv2 Datum Grid Shift
  Snow Data Assimilation System
  Swedish Grid RIK (.rik)
  USGS Optional ASCII DEM (and CDED)
  GeoSoft Grid Exchange Format
  Northwood Numeric Grid Format .grd/.tab
  Northwood Classified Grid Format .grc/.tab
  ARC Digitized Raster Graphics
  Standard Raster Product (ASRP/USRP)
  Magellan topo (.blx)
  SAGA GIS Binary Grid (.sdat)
  Kml Super Overlay
  ASCII Gridded XYZ
  HF2/HFZ heightfield raster
  OziExplorer Image File
  USGS LULC Composite Theme Grid
  Arc/Info Export E00 GRID
  ZMap Plus Grid
  NOAA NGS Geoid Height Grids

11.1.2. Creating rasters using PostGIS raster functions

On many occasions, you'll want to create rasters and raster tables right in the database. There are a plethora of functions to do that. The general steps to follow.

  1. Create a table with a raster column to hold the new raster records which can be accomplished with:

    CREATE TABLE myrasters(rid serial primary key, rast raster);
  2. There are many functions to help with that goal. If you are creating rasters not as a derivative of other rasters, you will want to start with: ST_MakeEmptyRaster , followed by ST_AddBand

    You can also create rasters from geometries. To achieve that you'll want to use ST_AsRaster perhaps accompanied with other functions such as ST_Union or ST_MapAlgebraFct or any of the family of other map algebra functions.

    There are even many more options for creating new raster tables from existing tables. For example you can create a raster table in a different projection from an existing one using ST_Transform

  3. Once you are done populating your table initially, you'll want to create a spatial index on the raster column with something like:

    CREATE INDEX myrasters_rast_st_convexhull_idx ON myrasters USING gist( ST_ConvexHull(rast) );

    Note the use of ST_ConvexHull since most raster operators are based on the convex hull of the rasters.


    Pre-2.0 versions of PostGIS raster were based on the envelop rather than the convex hull. For the spatial indexes to work properly you'll need to drop those and replace with convex hull based index.

  4. Apply raster constraints using AddRasterConstraints

11.1.3. Using "out db" cloud rasters

The raster2pgsql tool uses GDAL to access raster data, and can take advantage of a key GDAL feature: the ability to read from rasters that are stored remotely in cloud "object stores" (e.g. AWS S3, Google Cloud Storage).

Efficient use of cloud stored rasters requires the use of a "cloud optimized" format. The most well-known and widely used is the " cloud optimized GeoTIFF " format. Using a non-cloud format, like a JPEG, or an un-tiled TIFF will result in very poor performance, as the system will have to download the entire raster each time it needs to access a subset.

First, load your raster into the cloud storage of your choice. Once it is loaded, you will have a URI to access it with, either an "http" URI, or sometimes a URI specific to the service. (e.g., "s3://bucket/object"). To access non-public buckets, you will need to supply GDAL config options to authenticate your connection. Note that this command is reading from the cloud raster and writing to the database.

AWS_ACCESS_KEY_ID=xxxxxxxxxxxxxxxxxxxx \
AWS_SECRET_ACCESS_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx \
raster2pgsql \
  -s 990000 \
  -t 256x256 \
  -I \
  -R \
  /vsis3/ \
  your_table \
  | psql your_db

Once the table is loaded, you need to give the database permission to read from remote rasters, by setting two permissions, postgis.enable_outdb_rasters and postgis.gdal_enabled_drivers .

SET postgis.enable_outdb_rasters = true;
SET postgis.gdal_enabled_drivers TO 'ENABLE_ALL';

To make the changes sticky, set them directly on your database. You will need to re-connect to experience the new settings.

ALTER DATABASE your_db SET postgis.enable_outdb_rasters = true;
ALTER DATABASE your_db SET postgis.gdal_enabled_drivers TO 'ENABLE_ALL';

For non-public rasters, you may have to provide access keys to read from the cloud rasters. The same keys you used to write the raster2pgsql call can be set for use inside the database, with the postgis.gdal_config_options configuration. Note that multiple options can be set by space-separating the key=value pairs.

SET postgis.gdal_vsi_options = 'AWS_ACCESS_KEY_ID=xxxxxxxxxxxxxxxxxxxx

Once you have the data loaded and permissions set you can interact with the raster table like any other raster table, using the same functions. The database will handle all the mechanics of connecting to the cloud data when it needs to read pixel data.

11.2. Raster Catalogs

There are two raster catalog views that come packaged with PostGIS. Both views utilize information embedded in the constraints of the raster tables. As a result the catalog views are always consistent with the raster data in the tables since the constraints are enforced.

  1. raster_columns this view catalogs all the raster table columns in your database.

  2. raster_overviews this view catalogs all the raster table columns in your database that serve as overviews for a finer grained table. Tables of this type are generated when you use the -l switch during load.

11.2.1. Raster Columns Catalog

The raster_columns is a catalog of all raster table columns in your database that are of type raster. It is a view utilizing the constraints on the tables so the information is always consistent even if you restore one raster table from a backup of another database. The following columns exist in the raster_columns catalog.

If you created your tables not with the loader or forgot to specify the -C flag during load, you can enforce the constraints after the fact using AddRasterConstraints so that the raster_columns catalog registers the common information about your raster tiles.

  • r_table_catalog The database the table is in. This will always read the current database.

  • r_table_schema The database schema the raster table belongs to.

  • r_table_name raster table

  • r_raster_column the column in the r_table_name table that is of type raster. There is nothing in PostGIS preventing you from having multiple raster columns per table so its possible to have a raster table listed multiple times with a different raster column for each.

  • srid The spatial reference identifier of the raster. Should be an entry in the Section 4.5, “Spatial Reference Systems” .

  • scale_x The scaling between geometric spatial coordinates and pixel. This is only available if all tiles in the raster column have the same scale_x and this constraint is applied. Refer to ST_ScaleX for more details.

  • scale_y The scaling between geometric spatial coordinates and pixel. This is only available if all tiles in the raster column have the same scale_y and the scale_y constraint is applied. Refer to ST_ScaleY for more details.

  • blocksize_x The width (number of pixels across) of each raster tile . Refer to ST_Width for more details.

  • blocksize_y The width (number of pixels down) of each raster tile . Refer to ST_Height for more details.

  • same_alignment A boolean that is true if all the raster tiles have the same alignment . Refer to ST_SameAlignment for more details.

  • regular_blocking If the raster column has the spatially unique and coverage tile constraints, the value with be TRUE. Otherwise, it will be FALSE.

  • num_bands The number of bands in each tile of your raster set. This is the same information as what is provided by ST_NumBands

  • pixel_types An array defining the pixel type for each band. You will have the same number of elements in this array as you have number of bands. The pixel_types are one of the following defined in ST_BandPixelType .

  • nodata_values An array of double precision numbers denoting the nodata_value for each band. You will have the same number of elements in this array as you have number of bands. These numbers define the pixel value for each band that should be ignored for most operations. This is similar information provided by ST_BandNoDataValue .

  • out_db An array of boolean flags indicating if the raster bands data is maintained outside the database. You will have the same number of elements in this array as you have number of bands.

  • extent This is the extent of all the raster rows in your raster set. If you plan to load more data that will change the extent of the set, you'll want to run the DropRasterConstraints function before load and then reapply constraints with AddRasterConstraints after load.

  • spatial_index A boolean that is true if raster column has a spatial index.

11.2.2. Raster Overviews

raster_overviews catalogs information about raster table columns used for overviews and additional information about them that is useful to know when utilizing overviews. Overview tables are cataloged in both raster_columns and raster_overviews because they are rasters in their own right but also serve an additional special purpose of being a lower resolution caricature of a higher resolution table. These are generated along-side the main raster table when you use the -l switch in raster loading or can be generated manually using AddOverviewConstraints .

Overview tables contain the same constraints as other raster tables as well as additional informational only constraints specific to overviews.


The information in raster_overviews does not duplicate the information in raster_columns . If you need the information about an overview table present in raster_columns you can join the raster_overviews and raster_columns together to get the full set of information you need.

Two main reasons for overviews are:

  1. Low resolution representation of the core tables commonly used for fast mapping zoom-out.

  2. Computations are generally faster to do on them than their higher resolution parents because there are fewer records and each pixel covers more territory. Though the computations are not as accurate as the high-res tables they support, they can be sufficient in many rule-of-thumb computations.

The raster_overviews catalog contains the following columns of information.

  • o_table_catalog The database the overview table is in. This will always read the current database.

  • o_table_schema The database schema the overview raster table belongs to.

  • o_table_name raster overview table name

  • o_raster_column the raster column in the overview table.

  • r_table_catalog The database the raster table that this overview services is in. This will always read the current database.

  • r_table_schema The database schema the raster table that this overview services belongs to.

  • r_table_name raster table that this overview services.

  • r_raster_column the raster column that this overview column services.

  • overview_factor - this is the pyramid level of the overview table. The higher the number the lower the resolution of the table. raster2pgsql if given a folder of images, will compute overview of each image file and load separately. Level 1 is assumed and always the original file. Level 2 is will have each tile represent 4 of the original. So for example if you have a folder of 5000x5000 pixel image files that you chose to chunk 125x125, for each image file your base table will have (5000*5000)/(125*125) records = 1600, your (l=2) o_2 table will have ceiling(1600/Power(2,2)) = 400 rows, your (l=3) o_3 will have ceiling(1600/Power(2,3) ) = 200 rows. If your pixels aren't divisible by the size of your tiles, you'll get some scrap tiles (tiles not completely filled). Note that each overview tile generated by raster2pgsql has the same number of pixels as its parent, but is of a lower resolution where each pixel of it represents (Power(2,overview_factor) pixels of the original).

11.3. Building Custom Applications with PostGIS Raster

The fact that PostGIS raster provides you with SQL functions to render rasters in known image formats gives you a lot of options for rendering them. For example you can use OpenOffice / LibreOffice for rendering as demonstrated in Rendering PostGIS Raster graphics with LibreOffice Base Reports . In addition you can use a wide variety of languages as demonstrated in this section.

11.3.1. PHP Example Outputting using ST_AsPNG in concert with other raster functions

In this section, we'll demonstrate how to use the PHP PostgreSQL driver and the ST_AsGDALRaster family of functions to output band 1,2,3 of a raster to a PHP request stream that can then be embedded in an img src html tag.

The sample query demonstrates how to combine a whole bunch of raster functions together to grab all tiles that intersect a particular wgs 84 bounding box and then unions with ST_Union the intersecting tiles together returning all bands, transforms to user specified projection using ST_Transform , and then outputs the results as a png using ST_AsPNG .

You would call the below using


to get the raster image in Massachusetts state plane feet.

/** contents of test_raster.php **/
$conn_str ='dbname=mydb host=localhost port=5432 user=myuser password=mypwd';
$dbconn = pg_connect($conn_str);
header('Content-Type: image/png');
/**If a particular projection was requested use it otherwise use mass state plane meters **/
if (!empty( $_REQUEST['srid'] ) && is_numeric( $_REQUEST['srid']) ){
		$input_srid = intval($_REQUEST['srid']);
else { $input_srid = 26986; }
/** The set bytea_output may be needed for PostgreSQL 9.0+, but not for 8.4 **/
$sql = "set bytea_output='escape';
			ST_AddBand(ST_Union(rast,1), ARRAY[ST_Union(rast,2),ST_Union(rast,3)])
				,$input_srid) ) As new_rast
	 ST_Intersects(rast, ST_Transform(ST_MakeEnvelope(-71.1217, 42.227, -71.1210, 42.218,4326),26986) )";
$result = pg_query($sql);
$row = pg_fetch_row($result);
if ($row === false) return;
echo pg_unescape_bytea($row[0]);

11.3.2. ASP.NET C# Example Outputting using ST_AsPNG in concert with other raster functions

In this section, we'll demonstrate how to use Npgsql PostgreSQL .NET driver and the ST_AsGDALRaster family of functions to output band 1,2,3 of a raster to a PHP request stream that can then be embedded in an img src html tag.

You will need the npgsql .NET PostgreSQL driver for this exercise which you can get the latest of from . Just download the latest and drop into your ASP.NET bin folder and you'll be good to go.

The sample query demonstrates how to combine a whole bunch of raster functions together to grab all tiles that intersect a particular wgs 84 bounding box and then unions with ST_Union the intersecting tiles together returning all bands, transforms to user specified projection using ST_Transform , and then outputs the results as a png using ST_AsPNG .

This is same example as Section 11.3.1, “PHP Example Outputting using ST_AsPNG in concert with other raster functions” except implemented in C#.

You would call the below using


to get the raster image in Massachusetts state plane feet.

 -- web.config connection string section --
    <add name="DSN"
        connectionString="server=localhost;database=mydb;Port=5432;User Id=myuser;password=mypwd"/>
// Code for TestRaster.ashx
<%@ WebHandler Language="C#" Class="TestRaster" %>
using System;
using System.Data;
using System.Web;
using Npgsql;

public class TestRaster : IHttpHandler
	public void ProcessRequest(HttpContext context)

		context.Response.ContentType = "image/png";


	public bool IsReusable {
		get { return false; }

	public byte[] GetResults(HttpContext context)
		byte[] result = null;
		NpgsqlCommand command;
		string sql = null;
		int input_srid = 26986;
        try {
		    using (NpgsqlConnection conn = new NpgsqlConnection(System.Configuration.ConfigurationManager.ConnectionStrings["DSN"].ConnectionString)) {

                if (context.Request["srid"] != null)
                    input_srid = Convert.ToInt32(context.Request["srid"]);
                sql = @"SELECT ST_AsPNG(
                                ST_Union(rast,1), ARRAY[ST_Union(rast,2),ST_Union(rast,3)])
				                    ,:input_srid) ) As new_rast
                                    ST_Transform(ST_MakeEnvelope(-71.1217, 42.227, -71.1210, 42.218,4326),26986) )";
			    command = new NpgsqlCommand(sql, conn);
                command.Parameters.Add(new NpgsqlParameter("input_srid", input_srid));

			    result = (byte[]) command.ExecuteScalar();

        catch (Exception ex)
            result = null;
		return result;

11.3.3. Java console app that outputs raster query as Image file

This is a simple java console app that takes a query that returns one image and outputs to specified file.

You can download the latest PostgreSQL JDBC drivers from

You can compile the following code using a command something like:

set env CLASSPATH .:..\postgresql-9.0-801.jdbc4.jar
jar cfm SaveQueryImage.jar Manifest.txt *.class

And call it from the command-line with something like

java -jar SaveQueryImage.jar "SELECT ST_AsPNG(ST_AsRaster(ST_Buffer(ST_Point(1,5),10, 'quad_segs=2'),150, 150, '8BUI',100));" "test.png" 
 -- Manifest.txt --
Class-Path: postgresql-9.0-801.jdbc4.jar
Main-Class: SaveQueryImage
// Code for
import java.sql.Connection;
import java.sql.SQLException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;

public class SaveQueryImage {
  public static void main(String[] argv) {
      System.out.println("Checking if Driver is registered with DriverManager.");

      try {
        //java.sql.DriverManager.registerDriver (new org.postgresql.Driver());
      catch (ClassNotFoundException cnfe) {
        System.out.println("Couldn't find the driver!");

      Connection conn = null;

      try {
        conn = DriverManager.getConnection("jdbc:postgresql://localhost:5432/mydb","myuser", "mypwd");

        PreparedStatement sGetImg = conn.prepareStatement(argv[0]);

        ResultSet rs = sGetImg.executeQuery();

		FileOutputStream fout;
			/** Output to file name requested by user **/
			fout = new FileOutputStream(new File(argv[1]) );
		catch(Exception e)
			System.out.println("Can't create file");

      catch (SQLException se) {
        System.out.println("Couldn't connect: print out a stack trace and exit.");

11.3.4. Use PLPython to dump out images via SQL

This is a plpython stored function that creates a file in the server directory for each record. Requires you have plpython installed. Should work fine with both plpythonu and plpython3u.

CREATE OR REPLACE FUNCTION write_file (param_bytes bytea, param_filepath text)
AS $$
f = open(param_filepath, 'wb+')
return param_filepath
$$ LANGUAGE plpythonu;
--write out 5 images to the PostgreSQL server in varying sizes
-- note the postgresql daemon account needs to have write access to folder
-- this echos back the file names created;
 SELECT write_file(ST_AsPNG(
	ST_AsRaster(ST_Buffer(ST_Point(1,5),j*5, 'quad_segs=2'),150*j, 150*j, '8BUI',100)),
	 'C:/temp/slices'|| j || '.png')
	 FROM generate_series(1,5) As j;


11.3.5. Outputting Rasters with PSQL

Sadly PSQL doesn't have easy to use built-in functionality for outputting binaries. This is a bit of a hack that piggy backs on PostgreSQL somewhat legacy large object support. To use first launch your psql commandline connected to your database.

Unlike the python approach, this approach creates the file on your local computer.

SELECT oid, lowrite(lo_open(oid, 131072), png) As num_bytes
 ( VALUES (lo_create(0),
   ST_AsPNG( (SELECT rast FROM WHERE rid=1) )
  ) ) As v(oid,png);
-- you'll get an output something like --
   oid   | num_bytes
 2630819 |     74860

-- next note the oid and do this replacing the c:/test.png to file path location
-- on your local computer
 \lo_export 2630819 'C:/temp/aerial_samp.png'

-- this deletes the file from large object storage on db
SELECT lo_unlink(2630819);