gdal

Original：🇺🇸 English

Translated

This skill should be used when working with GDAL command line tools for raster and vector geospatial processing. Covers inspection, reprojection, clipping, raster conversion, mosaics, tiling, rasterization, and COG creation.

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Sourceisaaccorley/geospatial-skills

Added on2026-08-28

NPX Install

npx skill4agent add isaaccorley/geospatial-skills gdal

SKILL.md Content

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GDAL Skill

Use GDAL command line tools for common raster and vector geospatial workflows.

Tools

Prefer the smallest tool that fits the task:

```
gdalinfo
```
```
ogrinfo
```
```
gdalwarp
```
```
gdal_translate
```
```
gdalbuildvrt
```
```
gdaladdo
```
```
gdaltindex
```
```
gdal_rasterize
```
```
gdal2tiles.py
```
```
ogr2ogr
```

If GDAL is not installed, ask the user to install it before continuing.

Workflow

1. Inspect first

bash

gdalinfo INPUT.tif
ogrinfo INPUT.shp -so -al

One full

gdalinfo

dump on unknown data is worth it; after that, grep for the lines you need (

Size is|Origin|Pixel Size|Band|Overviews|ID\[

) — full WKT dumps are large. For vectors,

-so -al

prints the layer summary; bare

-so

without

-al

or a layer name prints nothing useful.

2. Pick raster vs vector path

Raster tasks:

gdalwarp

,

gdal_translate

,

gdalbuildvrt

,

gdal_rasterize

,

gdal2tiles.py

Vector tasks:
```
ogr2ogr
```
,
```
ogrinfo
```
,
```
gdaltindex
```

3. Write a new output

prefer new output files
preserve CRS and nodata intentionally
use compression for large rasters

4. Validate output

Use the check that matches the operation — improvising verification is where runs go wrong:

bash

# Any lossless conversion or mosaic: band checksums must match the input
gdalinfo -checksum INPUT.tif | grep Checksum
gdalinfo -checksum OUTPUT.tif | grep Checksum

# COG: quick layout check, then real validation
gdalinfo OUTPUT.tif | grep -E "LAYOUT|Block=|Overviews"
rio cogeo validate OUTPUT.tif   # via `uv run --with rio-cogeo` if not installed

# Warp/clip: CRS, resolution, per-band min/max in one call
gdalinfo -mm OUTPUT.tif

# Mosaic seamlessness: each quadrant bit-identical to its source tile
# (explicit offsets per tile — avoid shell arrays, the login shell may be zsh)
gdal_translate -q -of VRT -srcwin 0 0 256 256 MOSAIC.tif /tmp/quad.vrt
gdalinfo -checksum /tmp/quad.vrt | grep Checksum   # compare vs TILE_0.tif

# Vector: layer summary + SQLite-dialect counts
ogrinfo OUTPUT.gpkg LAYER -so
ogrinfo OUTPUT.gpkg -dialect SQLite -sql "SELECT COUNT(*) FROM layer"

gdaladdo

on a writable GeoTIFF builds internal overviews;

-ro

forces an external

.ovr

sidecar — so the absence of a

.ovr

file next to the output proves the overviews are internal.

Quick Reference

bash

# Inspect
gdalinfo INPUT.tif
ogrinfo INPUT.shp -so

# Vector conversion and clipping
ogr2ogr -f GeoJSON -t_srs epsg:4326 OUTPUT.geojson INPUT.shp
gdaltindex -t_srs epsg:4326 -f GeoJSON OUTPUT_EXTENT.geojson INPUT_RASTER.tif
ogr2ogr -f GeoJSON -clipsrc OUTPUT_EXTENT.geojson OUTPUT_CLIPPED.geojson INPUT.shp

# Raster reprojection (choose -r intentionally: bilinear/cubic for continuous, near for categorical)
gdalwarp -t_srs EPSG:XXXX -tr XRES YRES -r bilinear -co TILED=YES -co COMPRESS=DEFLATE -co PREDICTOR=2 INPUT.tif OUTPUT.tif

# Raster clipping
gdal_translate -srcwin XOFF YOFF XSIZE YSIZE INPUT.tif OUTPUT.tif   # by pixel window (exact, no resampling)
gdal_translate -projwin ULX ULY LRX LRY INPUT.tif OUTPUT.tif        # by georeferenced bounds
gdalwarp -cutline INPUT.shp -crop_to_cutline -dstalpha INPUT.tif OUTPUT.tif   # by polygon

# Raster conversion
gdal_translate -b 1 -b 2 -b 3 INPUT.tif OUTPUT.tif
gdal_translate -b 1 -b 2 -b 3 -of JPEG -outsize 400 0 INPUT.tif OUTPUT.jpg

# Mosaic and stack
gdalbuildvrt OUTPUT.vrt path/to/tiffs/*.tif
gdal_translate -co TILED=YES -co BIGTIFF=YES -co NUM_THREADS=ALL_CPUS -co COMPRESS=LZW -co PREDICTOR=2 OUTPUT.vrt OUTPUT.tif

# Internal overviews on an existing GeoTIFF (-r near for categorical; -ro writes external .ovr instead)
gdaladdo -r average OUTPUT.tif 2 4 8

# Rasterize and tile
gdal_rasterize -burn 1.0 -ot Byte -of GTiff -co COMPRESS=LZW -co BIGTIFF=YES INPUT.shp OUTPUT.tif
gdal2tiles.py -z 10-16 INPUT_BYTE.tif OUTPUT/

# COG (format-only conversion — smallest tool that fits)
gdal_translate -of COG -co COMPRESS=LZW -co PREDICTOR=2 -co NUM_THREADS=ALL_CPUS INPUT.tif OUTPUT.tif
# use gdalwarp -of COG only when also reprojecting in the same step

# Vector SQL: OGR's default dialect has no GROUP BY, window, or spatial functions —
# pass -dialect SQLite for anything beyond simple SELECT/WHERE
ogr2ogr -f GPKG OUT.gpkg IN.shp -dialect SQLite \
  -sql "SELECT *, ST_Area(ST_Transform(geometry, EPSG)) AS area_m2 FROM layer WHERE landuse IN ('ag')" \
  -t_srs EPSG:XXXX -nln layername

References

```
references/gdal-recipes.md
```
(the only reference file)

gdal

NPX Install

Tags

SKILL.md Content

GDAL Skill

Tools

Workflow

1. Inspect first

2. Pick raster vs vector path

3. Write a new output

4. Validate output

Quick Reference

References