Command Line Interface

# Display comprehensive dataset information
rio info [OPTIONS] INPUT

# Print geographic bounds of dataset  
rio bounds [OPTIONS] INPUT

# Show dataset block structure
rio blocks [OPTIONS] INPUT

# Print ground control points
rio gcps [OPTIONS] INPUT

# Show GDAL environment information
rio env [OPTIONS]

# Get detailed dataset information
rio info landsat_scene.tif
rio info --indent 2 landsat_scene.tif  # Pretty-printed JSON
rio info --count landsat_scene.tif     # Include pixel counts

# Get geographic bounds
rio bounds landsat_scene.tif
rio bounds --dst-crs EPSG:3857 landsat_scene.tif  # Transform bounds to different CRS

# Examine internal block structure  
rio blocks landsat_scene.tif
rio blocks --bidx 1 landsat_scene.tif  # Specific band only

# Check ground control points
rio gcps landsat_scene.tif

# Display environment information
rio env
rio env --formats  # Show supported formats

# Transform coordinates between CRS
rio transform [OPTIONS] INPUT

# Warp raster to new CRS or resolution  
rio warp [OPTIONS] INPUT OUTPUT

# Reproject raster dataset
rio reproject [OPTIONS] INPUT OUTPUT

# Transform point coordinates
echo "-120.5 35.2" | rio transform --dst-crs EPSG:3857 input.tif

# Transform with different precision
echo "-120.5 35.2" | rio transform --precision 2 --dst-crs EPSG:3857 input.tif

# Warp to new projection
rio warp --dst-crs EPSG:3857 --resampling bilinear input.tif output_mercator.tif

# Warp with custom resolution
rio warp --dst-crs EPSG:3857 --res 30 input.tif output_30m.tif

# Warp with specific bounds
rio warp --bounds -13580000 4200000 -13570000 4210000 input.tif cropped.tif

# Reproject with compression
rio reproject --dst-crs EPSG:4326 --compress lzw input.tif output_wgs84.tif

# Merge multiple rasters into mosaic
rio merge [OPTIONS] INPUT... OUTPUT

# Apply vector mask to raster
rio mask [OPTIONS] INPUT FEATURES OUTPUT

# Clip raster to geometry bounds  
rio clip [OPTIONS] INPUT FEATURES OUTPUT

# Raster calculator operations
rio calc [OPTIONS] COMMAND INPUT... OUTPUT

# Sample raster values at coordinates
rio sample [OPTIONS] INPUT

# Merge multiple GeoTIFF files
rio merge tile1.tif tile2.tif tile3.tif mosaic.tif
rio merge --bounds -180 -90 180 90 tiles/*.tif world_mosaic.tif

# Apply shapefile mask
rio mask --crop landsat_scene.tif study_area.shp masked_scene.tif
rio mask --invert landsat_scene.tif water_bodies.shp land_only.tif

# Clip to vector bounds
rio clip landsat_scene.tif study_area.shp clipped_scene.tif

# Calculator operations
rio calc "(+ (read 1) (read 2))" band1.tif band2.tif sum.tif
rio calc "(* (read 1) 2)" input.tif doubled.tif
rio calc "(where (> (read 1) 100) 1 0)" input.tif binary.tif

# Sample values at points
echo "-120.5 35.2" | rio sample input.tif
echo "-120.5 35.2\n-119.8 35.9" | rio sample --bidx 1,2,3 rgb_image.tif

# Convert between raster formats
rio convert [OPTIONS] INPUT OUTPUT

# Translate datasets (GDAL wrapper)
rio translate [OPTIONS] INPUT OUTPUT

# Stack bands from multiple files
rio stack [OPTIONS] INPUT... OUTPUT

# Convert format
rio convert input.tif output.jpg
rio convert --format PNG input.tif output.png

# Translate with options
rio translate --compress lzw --tiled input.tif output.tif
rio translate --dtype uint8 --scale-ratio 0.1 float_input.tif scaled_output.tif

# Stack bands into single file
rio stack red.tif green.tif blue.tif rgb_composite.tif
rio stack --bidx 1 *.tif stacked_band1.tif

# Extract polygon shapes from raster
rio shapes [OPTIONS] INPUT

# Rasterize vector features  
rio rasterize [OPTIONS] FEATURES INPUT OUTPUT

# Extract shapes to GeoJSON
rio shapes landcover.tif > landcover_polygons.geojson
rio shapes --mask nodata_mask.tif --precision 6 landcover.tif > shapes.geojson

# Rasterize shapefile
rio rasterize --like template.tif polygons.shp rasterized.tif
rio rasterize --dimensions 1000 1000 --res 0.001 points.shp density.tif
rio rasterize --burn 1 --all-touched lines.shp line_raster.tif

# Build overview images
rio overview [OPTIONS] INPUT

# Edit dataset metadata  
rio edit-info [OPTIONS] INPUT

# Build overviews with default settings
rio overview build input.tif

# Build overviews with specific resampling  
rio overview build --resampling average input.tif

# Build overviews with custom factors
rio overview build --factors 2,4,8,16 input.tif

# List existing overviews
rio overview list input.tif

# Edit dataset information
rio edit-info --crs EPSG:4326 input.tif
rio edit-info --nodata -9999 input.tif
rio edit-info --tag "DESCRIPTION=Processed satellite imagery" input.tif

# Create new raster dataset  
rio create [OPTIONS] OUTPUT

# Inspect dataset interactively
rio insp [OPTIONS] INPUT

# Create new empty raster
rio create --driver GTiff --width 1000 --height 1000 --count 3 --dtype uint8 --crs EPSG:4326 new_raster.tif

# Inspect dataset in interactive mode
rio insp landsat_scene.tif

# Remove raster files and associated files
rio rm [OPTIONS] INPUT...

# Remove single file and auxiliaries
rio rm dataset.tif

# Remove multiple files
rio rm tile*.tif

# Remove with confirmation
rio rm --dry-run large_dataset.tif  # Show what would be deleted

# Global options (available for most commands)
--verbose, -v          # Increase verbosity
--quiet, -q           # Suppress output  
--format DRIVER       # Output format driver
--co NAME=VALUE       # Creation options
--compress METHOD     # Compression method
--dtype TYPE          # Data type
--nodata VALUE        # NoData value
--crs CRS            # Coordinate reference system
--resampling METHOD   # Resampling algorithm
--threads N          # Number of processing threads

# Processing pipeline example
# 1. Get dataset info
rio info --count input.tif

# 2. Warp to standard projection  
rio warp --dst-crs EPSG:3857 --compress lzw input.tif warped.tif

# 3. Apply mask and crop
rio mask --crop warped.tif study_area.shp masked.tif

# 4. Calculate vegetation index
rio calc "(/ (- (read 4) (read 3)) (+ (read 4) (read 3)))" masked.tif ndvi.tif

# 5. Extract high NDVI areas
rio calc "(where (> (read 1) 0.5) (read 1) 0)" ndvi.tif high_ndvi.tif

# 6. Convert to shapes
rio shapes --mask high_ndvi.tif high_ndvi.tif > vegetation_polygons.geojson

# Batch processing with shell scripting
for file in *.tif; do
    output="${file%.tif}_processed.tif"
    rio warp --dst-crs EPSG:4326 --compress lzw "$file" "$output"
done

# Piping coordinates for sampling
cat coordinates.txt | rio sample --bidx 1,2,3 landsat.tif > sampled_values.txt

# Using with other tools
rio bounds *.tif | jq -s 'map(select(.bounds)) | .[].bounds' > all_bounds.json

# Common format examples
rio convert input.tif output.jpg        # JPEG
rio convert input.tif output.png        # PNG  
rio convert input.tif output.nc         # NetCDF
rio convert --format COG input.tif output.tif  # Cloud Optimized GeoTIFF
rio convert --format GTiff --co TILED=YES input.tif tiled.tif  # Tiled GeoTIFF

# List available formats
rio env --formats

# Check command success
rio info nonexistent.tif
echo $?  # Returns non-zero exit code on error

# Verbose error information
rio --verbose warp invalid_crs.tif output.tif

# Dry run mode (where available)
rio overview build --dry-run input.tif  # Show what would be done