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GDAL/OGR Cheat Sheet

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# gdal/ogr cheatsheet

## GDAL

#### Cloud Optimized GeoTif - [COG](https://www.cogeo.org/)

gdal_translate in.tif out.tif -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=LZW
    
- gdaladdo should be run before to create internal overviews

Source: https://trac.osgeo.org/gdal/wiki/CloudOptimizedGeoTIFF#HowtogenerateitwithGDAL

alternatively use ```-of COG``` :

gdal_translate world.tif world_webmerc_cog.tif -of COG -co TILING_SCHEME=GoogleMapsCompatible -co COMPRESS=JPEG
    
Source: https://gdal.org/drivers/raster/cog.html
    
*as of GDAL>=2.4 [WebP](https://gdal.org/drivers/raster/webp.html) seems to be the best compression available, 
 although it seem QGIS 3.4 (GDAL 2.2.2, released 2017/09/15) does not read this type of compression yet

-CO COMPRESS=WEBP

#### Compress imagery

as seen on http://blog.cleverelephant.ca/2015/02/geotiff-compression-for-dummies.html

gdal_translate \
        -co COMPRESS=JPEG \
        -co PHOTOMETRIC=YCBCR \
        -co TILED=YES \
        -co BIGTIFF=YES \
        5255C.tif 5255C_JPEG_YCBCR.tif

* If dealing with IR bands the recommended options are -co PHOTOMETRIC=RGB -co ALPHA=NO
    source: https://lists.osgeo.org/pipermail/gdal-dev/2011-April/028455.html
    With these 2 options you should get the 4th band defined as 'Undefined'. Otherwise you get a 4th band defined as Alpha.

Create overviews

gdaladdo \
        --config COMPRESS_OVERVIEW JPEG \
        --config PHOTOMETRIC_OVERVIEW YCBCR \
        --config INTERLEAVE_OVERVIEW PIXEL \
        -r average \
        5255C_JPEG_YCBCR.tif \
        2 4 8 16
        
Compress a bunch of tifs...

for FILE in *.tif \
        do \
        BASE=`basename $FILE .tif` \
        NEWFILE=${BASE}_c.tif \
        gdal_translate -b 1 -b 2 -b 3 -co COMPRESS=JPEG -co TILED=YES -co PHOTOMETRIC=YCBCR $FILE $NEWFILE \
        done \
   
                
   
   
---
#### GDALWARP:

*see below for using gdalwarp to merge tifs
    
        gdalwarp -s_srs EPSG:4326 -t_srs EPSG:27700 home_wgs84.bmp home_OSGB36.tif
   
  Crops image based on shapefile select polygon using -cwhere
    
        gdalwarp -cutline shpfile.shp -cwhere "fieldname = 'fieldvalue'" -crop_to_cutline inimage.tif outimage.tif
                
  Mask raster to cutline, use NO_GEOTRANSFORM for un-georeferenced images
    
        gdalwarp -to SRC_METHOD=NO_GEOTRANSFORM -to DST_METHOD=NO_GEOTRANSFORM -cutline cut_line.csv in.tif out.tif

* Format of cut_line.csv
                id,WKT
                1,"POLYGON((9756 9321, 9756 360, 816 360, 816 9321, 9756 9321))"
    
                * options:
                -dstalpha to create an alpha band, masking nodata pixels
                -cblend <pixels> to feather eadges of imagery for a better seamless image
                -multi to multi-threaded processing
    
    
  Subset with -te 
    
        gdalwarp -te -7.35 48.48 3.79 59.51 merged_DEM.tif subset_DEM.tif

Change Resolution w/ -tr (target resolution)

gdalwarp -tr 100.0 100.0 -r cubic -of GTiff USGS_13_n45w068_20230302_6350.tif ./warped/USGS_13_n45w068_20230302_6350_100x100.tif
            
  Mosaic

gdalwarp --config GDAL_CACHEMAX 3000 -wm 3000 *.tif final_mosaic.tif

* Note that it is usually a good idea to "optimise" the resulting image with gdal_translate.

#### gdalbuildvrt
 
       gdalbuildvrt -input_file_list newfile output.vrt

then create overviews
 
      gdaladdo -ro --config COMPRESS_OVERVIEW JPEG --config PHOTOMETRIC_OVERVIEW YCBCR --config INTERLEAVE_OVERVIEW PIXEL --config BIGTIFF_OVERVIEW YES output.vrt 2 4 8 16
 
 * prepend ```/vsicurl/``` to your COG url in S3 to create VRT - https://www.cogeo.org/qgis-tutorial.html

---
#### gdal_translate

Compress tif

gdal_translate -of GTiff -co COMPRESS=DEFLATE -co TILED=NO image1.tif image1_compressed.tif
        
        * optional argument to resize image -outsize 50% 50%
        *for GEOTIFF compression option -co NUM_THREADS=ALL_CPUS is available for better preformance

Convert Multi-band GeoTiff file to JPEG:

gdal_translate -of JPEG 884084-utm.tif 884084-utm.jpg

If GDAL complains about strange tags used in a tif file (http://www.gdal.org/frmt_gtiff.html)

gdal_translate -co < PROFILE=BASELINE > or <PROFILE=GeoTIFF> input.tif output.tif
        
  Save a single band from a multi-band image

gdal_translate -b 1 input7.tif output.tif
   
  Re-order bands 
    source: https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-4-working-with-satellite-data-d3835b5e2971
 
       # 5 band image to 3 band RGB image \
       gdal_translate 1155205_2017-03-31_RE3_3A.tif \
       1155205_2017-03-31_RE3_3A_rgb.tif \
       -b 3 -b 2 -b 1 \
       -co COMPRESS=DEFLATE -co PHOTOMETRIC=RGB
   
   
---
#### gdal_contour:

gdal_contour -a elev dem.tif contour.shp -i 10.0

---
#### gdal_merge
    
  Merge DEMs

gdal_merge srtm_35_01.tif srtm_35_02.tif srtm_35_03.tif srtm_36_01.tif srtm_36_02.tif srtm_37_02.tif -o merged_DEM.tif

Merge Rasters:

gdal_merge -o Theale_merged.tif Theale1_cal.bmp Theale2_cal.bmp Theale3_cal.bmp Theale4_cal.bmp
        
  Merge Separate Bands into a single file: (band order matters) 
  source: (https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-4-working-with-satellite-data-d3835b5e2971)
  
        gdal_merge.py -o final_image.tif -separate image_B4.TIF iamge_B3.TIF image_B2.TIF -co PHOTOMETRIC=RGB -co COMPRESS=DEFLATE
   
   
--- 
  Copy all tifs to new location

for %I in (image1.tif image2.tif image3.tif image4.tif) \
        do \
        copy %I test\folder\

---
#### gdal2tiles - Create web map tiles

gdal2tiles.py --zoom=11-15 --title=maptitle in.tif output_folder_name

---
#### gdal polygonize

gdal_polygonize Project_clip1.tif -f "ESRI Shapefile" vector.shp crit2 Value
        
---        
#### gdal calc
  source: http://www.gdal.org/gdal_calc.html
  
        Average 2 bands together
        gdal_calc.py -A input.tif -B input2.tif --outfile=result.tif --calc="(A+B)/2"

---

## OGR

#### SUBSET SHAPEFILE:

ogr2ogr -spat -1.5 51 -0.5 52 -f "ESRI Shapefile" watersubset.shp waterways.shp

---
#### MEND SHAPEFILE:

ogr2ogr -skipfailures -f "ESRI Shapefile" mended.shp broken.shp

---
#### KML to SHP:

ogr2ogr -f "ESRI Shapefile" thames.shp thames.kml

---
#### CONVERT SHAPE TO KML and change proj:

ogr2ogr -f "KML" -s_srs "EPSG:27700" -t_srs  "EPSG:4326" rail.kml rail_OSGB36.shp

---
#### Convert shp to kml w/ descriptions:

ogr2ogr -f "KML" sample.kml sample.shp -dsco NameField=Field1 -dsco DescriptionField=field2

---
#### Convert shp to kml - using "where" as query featuures:

ogr2ogr -f "KML" -where "NBRHOOD='Telegraph Hill'" realtor_neighborhoods.kml realtor_neighborhoods.shp

---
#### Convert shp to kml - using "select" to add specific attributes:

ogr2ogr –SELECT “field1 field2 field3” -t_srs EPSG:4326 -f "KML" outPutFileName.kml inPutFileName.shp

---
#### REPROJECT SHAPE FILE:

ogr2ogr -s_srs "EPSG:4326" -t_srs "EPSG:27700" buildings_OS.shp buildings.shp

---
#### Get information about a shapefile: (List Fields and type)

ogrinfo -al ssurgo_geo.shp **This Lists ALL geometry which can be a pain

ogrinfo -al -geom=NO ssurgo_geo.shp **Will not list millions of pages of geometry

---
#### Extract all polygons from a SSURGO shapefile where the mapunit symbol is 'ScA':

ogr2ogr -where "musym = 'ScA' " ssurgo_ScA.shp ssurgo_utm.shp 
    
    
---    
#### GPX files
  source: http://www.gdal.org/ogr/drv_gpx.html
    
      ogr2ogr --config GPX_SHORT_NAMES YES out input.gpx track_points
    
  * GPX_SHORT_NAMES YES = converts long column names to shorter names to prevent non-unique names
  * out = is the ouput location and folder
  * track_points = the feature type you are converting/extracting from the file. Other options are: waypoints, route_points, routes,     tracks. If nothing is specified then all are extracted. An empty shp file is created for those with no features.

---
#### iterate over many files linux/unix (source: http://gothos.info/tag/gdal-ogr/)

#!/bin/bash
    from Sherman (2008) Desktop GIS Mapping the Planet With Open Source Tools pp 243-44

for shp in *.shp \
    do \
    echo “Processing $shp” \
    ogr2ogr -f “ESRI Shapefile” -t_srs EPSG:4326 geo/$shp $shp \
    done \

---
#### Ogr with SQL

source: http://www.sarasafavi.com/intro-to-ogr-part-i-exploring-data.html

ogrinfo city_of_austin_parks.shp -sql "SELECT COUNT(*) FROM city_of_austin_parks"
    
    * add '-so' for summary only

SQL and look at only one feature
    
    ogrinfo -q city_of_austin_parks.shp -sql "SELECT * FROM city_of_austin_parks" -fid 1
    
    * '-q' = quiet

Same using all SQL

ogrinfo -q city_of_austin_parks.shp -sql "SELECT * FROM city_of_austin_parks WHERE fid IN (1,3)"

---

#### Import into Postgis (from PostGIS Cookbook, 2014)

ogr2ogr -f PostgreSQL -sql "SELECT ISO2, NAME AS country_name \
        FROM 'TM_WORLD_BORDERS-0.3' WHERE REGION=2" \
        -nlt MULTIPOLYGON \
        PG:"host=000.000.000.000 dbname='postgis_cookbook' user='me' password='mypassword'" \
        -nln africa_countries \
        -lco SCHEMA=chp01 \
        -lco GEOMETRY_NAME=the_geom \
        TM_WORLD_BORDERS-0.3.shp

#### Export from Postgis (from PostGIS Cookbook, 2014)

ogr2ogr -f GeoJSON -t_srs EPSG:4326 warmest_hs.geojson \
        PG:"host=000.000.000.000 dbname='postgis_cookbook' user='me' password='mypassword'" \
        -sql "SELECT f.the_geom as the_geom, f.bright_t31, ac.iso2, ac.country_name \
        FROM chp01.global_24h as f \
        JOIN chp01.africa_countries as ac \
        ON ST_Contains(ac.the_geom, ST_Transform(f.the_geom, 4326)) \
        ORDER BY f.bright_t31 DESC LIMIT 100"
        
---

#### Using OGR with GNU Parallel

Source:http://blog.faraday.io/how-to-crunch-lots-of-geodata-in-parallel/
    
    mkdir wgs84  
    ls *.shp | parallel ogr2ogr -t_srs 'EPSG:4326' wgs84/{} {}

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