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https://github.com/dwtkns/gdal-cheat-sheet

Cheat sheet for GDAL/OGR command-line tools
https://github.com/dwtkns/gdal-cheat-sheet

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Cheat sheet for GDAL/OGR command-line tools

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README 

        
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    



__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](http://postgis.net/docs/manual-2.0/reference.html#Spatial_Relationships_Measurements) between the features. Fields from the join layer can be [aggregated](https://www.sqlite.org/lang_aggfunc.html) 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     \

	-a_srs EPSG:4269 input.png output.tif



__Clip raster by bounding box__



	gdalwarp -te     input.tif clipped_output.tif

	

__Clip raster to SHP / NoData for pixels beyond polygon boundary__



	gdalwarp -dstnodata  -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  -dstnodata 



__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   -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    



__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`](http://epsg.io/4326)). Use the correct code for your data.



__MODIS operations__



First, download relevant .hdf tiles from the MODIS ftp site: ; use the [MODIS sinusoidal grid](http://www.geohealth.ou.edu/modis_v5/modis.shtml) for reference.



List MODIS Subdatasets in a given HDF (conf. the [MODIS products table](https://lpdaac.usgs.gov/products/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](http://linfiniti.com/2009/09/clipping-rasters-with-gdal-using-polygons/)).



	function gdal_extent() {

		if [ -z "$1" ]; then 

			echo "Missing arguments. Syntax:"

			echo "  gdal_extent "

	    	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 "

	    	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  "

	    	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

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