Awesome
Cheat sheet for GDAL/OGR command-line geodata tools
Vector operations
Get vector information
ogrinfo -so input.shp layer-name
Or, for all layers
ogrinfo -al -so input.shp
Print vector extent
ogrinfo input.shp layer-name | grep Extent
List vector drivers
ogr2ogr --formats
Convert between vector formats
ogr2ogr -f "GeoJSON" output.json input.shp
Print count of features with attributes matching a given pattern
ogrinfo input.shp layer-name | grep "Search Pattern" | sort | uniq -c
Read from a zip file
This assumes that archive.zip is in the current directory. This example just extracts the file, but any ogr2ogr operation should work. It's also possible to write to existing zip files.
ogr2ogr -f 'GeoJSON' dest.geojson /vsizip/archive.zip/zipped_dir/in.geojson
Clip vectors by bounding box
ogr2ogr -f "ESRI Shapefile" output.shp input.shp -clipsrc <x_min> <y_min> <x_max> <y_max>
Clip one vector by another
ogr2ogr -clipsrc clipping_polygon.shp output.shp input.shp
Reproject vector:
ogr2ogr output.shp -t_srs "EPSG:4326" input.shp
Add an index to a shapefile
Add an index on an attribute:
ogrinfo example.shp -sql "CREATE INDEX ON example USING fieldname"
Add a spatial index:
ogrinfo example.shp -sql "CREATE SPATIAL INDEX ON example"
Merge features in a vector file by attribute ("dissolve")
ogr2ogr -f "ESRI Shapefile" dissolved.shp input.shp -dialect sqlite -sql "select ST_union(Geometry),common_attribute from input GROUP BY common_attribute"
Merge features ("dissolve") using a buffer to avoid slivers
ogr2ogr -f "ESRI Shapefile" dissolved.shp input.shp -dialect sqlite \
-sql "select ST_union(ST_buffer(Geometry,0.001)),common_attribute from input GROUP BY common_attribute"
Merge vector files:
ogr2ogr merged.shp input1.shp
ogr2ogr -update -append merged.shp input2.shp -nln merged
Extract from a vector file based on query
To extract features with STATENAME 'New York','New Hampshire', etc. from states.shp
ogr2ogr -where 'STATENAME like "New%"' states_subset.shp states.shp
To extract type 'pond' from water.shp
ogr2ogr -where "type = pond" ponds.shp water.shp
Subset & filter all shapefiles in a directory
Assumes that filename and name of layer of interest are the same...
basename -s.shp *.shp | xargs -n1 -I % ogr2ogr %-subset.shp %.shp -sql "SELECT field-one, field-two FROM '%' WHERE field-one='value-of-interest'"
Extract data from a PostGis database to a GeoJSON file
ogr2ogr -f "GeoJSON" file.geojson PG:"host=localhost dbname=database user=user password=password" \
-sql "SELECT * from table_name"
Extract data from an ESRI REST API
Services that use ESRI maps are sometimes powered by a REST server that can provide data in OGR can consume. Finding the correct end point can be tricky and may take some false starts.
ogr2ogr -f GeoJSON output.geojson "http:/example.com/arcgis/rest/services/SERVICE/LAYER/MapServer/0/query?f=json&returnGeometry=true&etc=..." OGRGeoJSON
Get the difference between two vector files
Given two files that both have an id field, this will produce a vector file with the part of file1.shp that doesn't intersect with file2.shp:
ogr2ogr diff.shp file1.shp -dialect sqlite \
-sql "SELECT ST_Difference(a.Geometry, b.Geometry) AS Geometry, a.id \
FROM file1 a LEFT JOIN 'file2.shp'.file2 b USING (id) WHERE a.Geometry != b.Geometry"
This assumes that file2.shp and file2.shp are both in the current directory.
Spatial join:
A spatial join transfers properties from one vector layer to another based on a spatial relationship between the features. Fields from the join layer can be aggregated in the output.
Given a set of points (trees.shp) and a set of polygons (parks.shp) in the same directory, create a polygon layer with the geometries from parks.shp and summaries of some columns in trees.shp:
ogr2ogr -f 'ESRI Shapefile' output.shp parks.shp -dialect sqlite \
-sql "SELECT p.Geometry, p.id id, SUM(t.field1) field1_sum, AVG(t.field2) field2_avg
FROM parks p, 'trees.shp'.trees t WHERE ST_Contains(p.Geometry, t.Geometry) GROUP BY p.id"
Note that features that from parks.shp that don't overlap with trees.shp won't be in the new file.
Raster operations
Get raster information
gdalinfo input.tif
List raster drivers
gdal_translate --formats
Force creation of world file (requires libgeotiff)
listgeo -tfw mappy.tif
Report PROJ.4 projection info, including bounding box (requires libgeotiff)
listgeo -proj4 mappy.tif
Convert between raster formats
gdal_translate -of "GTiff" input.grd output.tif
Convert 16-bit bands (Int16 or UInt16) to Byte type
(Useful for Landsat 8 imagery...)
gdal_translate -of "GTiff" -co "COMPRESS=LZW" -scale 0 65535 0 255 -ot Byte input_uint16.tif output_byte.tif
You can change '0' and '65535' to your image's actual min/max values to preserve more color variation or to apply the scaling to other band types - find that number with:
gdalinfo -mm input.tif | grep Min/Max
Convert a directory of raster files of the same format to another raster format
basename -s.img *.img | xargs -n1 -I % gdal_translate -of "GTiff" %.img %.tif
Reproject raster:
gdalwarp -t_srs "EPSG:102003" input.tif output.tif
Be sure to add -r bilinear if reprojecting elevation data to prevent funky banding artifacts.
Georeference an unprojected image with known bounding coordinates:
gdal_translate -of GTiff -a_ullr <top_left_lon> <top_left_lat> <bottom_right_lon> <bottom_right_lat> \
-a_srs EPSG:4269 input.png output.tif
Clip raster by bounding box
gdalwarp -te <x_min> <y_min> <x_max> <y_max> input.tif clipped_output.tif
Clip raster to SHP / NoData for pixels beyond polygon boundary
gdalwarp -dstnodata <nodata_value> -cutline input_polygon.shp input.tif clipped_output.tif
Crop raster dimensions to vector bounding box
gdalwarp -cutline cropper.shp -crop_to_cutline input.tif cropped_output.tif
Merge rasters
gdal_merge.py -o merged.tif input1.tif input2.tif
Alternatively,
gdalwarp input1.tif input2.tif merged.tif
Or, to preserve nodata values:
gdalwarp input1.tif input2.tif merged.tif -srcnodata <nodata_value> -dstnodata <merged_nodata_value>
Stack grayscale bands into a georeferenced RGB
Where LC81690372014137LGN00 is a Landsat 8 ID and B4, B3 and B2 correspond to R,G,B bands respectively:
gdal_merge.py -co "PHOTOMETRIC=RGB" -separate LC81690372014137LGN00_B{4,3,2}.tif -o LC81690372014137LGN00_rgb.tif
Fix an RGB TIF whose bands don't know they're RGB
gdal_merge.py -co "PHOTOMETRIC=RGB" input.tif -o output_rgb.tif
Export a raster for Google Earth
gdal_translate -of KMLSUPEROVERLAY input.tif output.kmz -co FORMAT=JPEG
Raster calculation (map algebra)
Average two rasters:
gdal_calc.py -A input1.tif -B input2.tif --outfile=output.tif --calc="(A+B)/2"
Add two rasters:
gdal_calc.py -A input1.tif -B input2.tif --outfile=output.tif --calc="A+B"
etc.
Create a hillshade from a DEM
gdaldem hillshade -of PNG input.tif hillshade.png
Change light direction:
gdaldem hillshade -of PNG -az 135 input.tif hillshade_az135.png
Use correct vertical scaling in meters if input is projected in degrees
gdaldem hillshade -s 111120 -of PNG input_WGS1984.tif hillshade.png
Apply color ramp to a DEM
First, create a color-ramp.txt file:
(Height, Red, Green, Blue)
0 110 220 110
900 240 250 160
1300 230 220 170
1900 220 220 220
2500 250 250 250
Then apply those colors to a DEM:
gdaldem color-relief input.tif color_ramp.txt color-relief.tif
Create slope-shading from a DEM
First, make a slope raster from DEM:
gdaldem slope input.tif slope.tif
Second, create a color-slope.txt file:
(Slope angle, Red, Green, Blue)
0 255 255 255
90 0 0 0
Finally, color the slope raster based on angles in color-slope.txt:
gdaldem color-relief slope.tif color-slope.txt slopeshade.tif
Resample (resize) raster
gdalwarp -ts <width> <height> -r cubic dem.tif resampled_dem.tif
Entering 0 for either width or height guesses based on current dimensions.
Alternatively,
gdal_translate -outsize 10% 10% -r cubic dem.tif resampled_dem.tif
For both of these, -r cubic specifies cubic interpolation: when resampling continuous data (like a DEM), the default nearest neighbor interpolation can result in "stair step" artifacts.
Burn vector into raster
gdal_rasterize -b 1 -i -burn -32678 -l layername input.shp input.tif
Extract polygons from raster
gdal_polygonize.py input.tif -f "GeoJSON" output.json
Create contours from DEM
gdal_contour -a elev -i 50 input_dem.tif output_contours.shp
Get values for a specific location in a raster
gdallocationinfo -xml -wgs84 input.tif <lon> <lat>
Convert GRIB band to .tif
Assumes data for entire globe in WGS84. Be sure to specify band, or you may end up with a nonsense RGB image composed of the first three.
gdal_translate input.grib -a_ullr -180 -90 180 90 -a_srs EPSG:4326 -b 1 output_band1.tif
Other
Convert KML points to CSV (simple)
ogr2ogr -f CSV output.csv input.kmz -lco GEOMETRY=AS_XY
Convert KML to CSV (WKT)
First list layers in the KML file
ogrinfo -so input.kml
Convert the desired KML layer to CSV
ogr2ogr -f CSV output.csv input.kml -sql "select *,OGR_GEOM_WKT from some_kml_layer"
CSV points to SHP
Given input.csv:
lon,lat,value
-81,31,13
-80,32,14
-81,33,15
Create a shapefile, using Spatialite functions to generate the point:
ogr2ogr output.shp input.csv -dialect sqlite \
-sql "SELECT MakePoint(CAST(lon as REAL), CAST(lat as REAL), 4326) Geometry, * FROM input"
Note the 4326, which refers to a spatial reference (in this case EPSG:4326). Use the correct code for your data.
MODIS operations
First, download relevant .hdf tiles from the MODIS ftp site: ftp://ladsftp.nascom.nasa.gov/; use the MODIS sinusoidal grid for reference.
List MODIS Subdatasets in a given HDF (conf. the MODIS products table)
gdalinfo longFileName.hdf | grep SUBDATASET
Make TIFs from each file in list; replace 'MOD12Q1:Land_Cover_Type_1' with desired Subdataset name
mkdir output
find . '*.hdf' -exec gdalwarp -of GTiff 'HDF4_EOS:EOS_GRID:"{}":MOD12Q1:Land_Cover_Type_1' output/{}.tif \;
Merge all .tifs in output directory into single file
gdal_merge.py -o output/Merged_Landcover.tif output/*.tif
BASH functions
Size Functions
This size function echos the pixel dimensions of a given file in the format expected by gdalwarp.
function gdal_size() {
SIZE=$(gdalinfo $1 |\
grep 'Size is ' |\
cut -d\ -f3-4 |\
sed 's/,//g')
echo -n "$SIZE"
}
This can be used to easily resample one raster to the dimensions of another:
gdalwarp -ts $(gdal_size bigraster.tif) -r cubicspline smallraster.tif resampled_smallraster.tif
Extent Functions
These extent functions echo the extent of the given file in the order/format expected by gdal_translate -projwin.
(Originally from Linfiniti).
function gdal_extent() {
if [ -z "$1" ]; then
echo "Missing arguments. Syntax:"
echo " gdal_extent <input_raster>"
return
fi
EXTENT=$(gdalinfo $1 |\
grep "Upper Left\|Lower Right" |\
sed "s/Upper Left //g;s/Lower Right //g;s/).*//g" |\
tr "\n" " " |\
sed 's/ *$//g' |\
tr -d "[(,]")
echo -n "$EXTENT"
}
function ogr_extent() {
if [ -z "$1" ]; then
echo "Missing arguments. Syntax:"
echo " ogr_extent <input_vector>"
return
fi
EXTENT=$(ogrinfo -al -so $1 |\
grep Extent |\
sed 's/Extent: //g' |\
sed 's/(//g' |\
sed 's/)//g' |\
sed 's/ - /, /g')
EXTENT=`echo $EXTENT | awk -F ',' '{print $1 " " $4 " " $3 " " $2}'`
echo -n "$EXTENT"
}
function ogr_layer_extent() {
if [ -z "$2" ]; then
echo "Missing arguments. Syntax:"
echo " ogr_extent <input_vector> <layer_name>"
return
fi
EXTENT=$(ogrinfo -so $1 $2 |\
grep Extent |\
sed 's/Extent: //g' |\
sed 's/(//g' |\
sed 's/)//g' |\
sed 's/ - /, /g')
EXTENT=`echo $EXTENT | awk -F ',' '{print $1 " " $4 " " $3 " " $2}'`
echo -n "$EXTENT"
}
Extents can be passed directly into a gdal_translate command like so:
gdal_translate -projwin $(ogr_extent boundingbox.shp) input.tif clipped_output.tif
or
gdal_translate -projwin $(gdal_extent target_crop.tif) input.tif clipped_output.tif
This can be a useful way to quickly crop one raster to the same extent as another. Add these to your ~/.bash_profile file for easy terminal access.
Sources
http://live.osgeo.org/en/quickstart/gdal_quickstart.html
https://github.com/nvkelso/geo-how-to/wiki/OGR-to-reproject,-modify-Shapefiles
ftp://ftp.remotesensing.org/gdal/presentations/OpenSource_Weds_Andre_CUGOS.pdf
http://linfiniti.com/2010/12/a-workflow-for-creating-beautiful-relief-shaded-dems-using-gdal/
http://linfiniti.com/2009/09/clipping-rasters-with-gdal-using-polygons/
http://nautilus.baruch.sc.edu/twiki_dmcc/bin/view/Main/OGR_example
http://www.gdal.org/frmt_hdf4.html
http://planetflux.adamwilson.us/2010/06/modis-processing-with-r-gdal-and-nco.html
http://trac.osgeo.org/gdal/wiki/FAQRaster
http://dirkraffel.com/2011/07/05/best-way-to-merge-color-relief-with-shaded-relief-map/
http://gfoss.blogspot.com/2008/06/gdal-raster-data-tips-and-tricks.html
http://osgeo-org.1560.x6.nabble.com/gdal-dev-Dissolve-shapefile-using-GDAL-OGR-td5036930.html
https://www.mapbox.com/tilemill/docs/guides/terrain-data/