Process raster

You are helping the user process geospatial raster data using geoai.

Input: $@

Follow these steps in order.

Step 1 -- Determine the operation

Parse $@ to identify the requested operation:

Operation	Triggers	Required inputs
clip	"clip", "crop", "subset", --bbox present	input raster + bbox
stack	"stack", "combine bands"	list of input rasters
mosaic	"mosaic", "merge"	input directory or list of rasters
raster-to-vector	"to vector", "vectorize", "polygonize"	input raster
vector-to-raster	"to raster", "rasterize", "burn"	input vector + pixel size

If the operation is unclear from the input, ask the user to specify.

Step 2 -- Resolve input file(s)

For single-file operations (clip, raster-to-vector, vector-to-raster):

find "$PWD" -name "INPUT_FILENAME" -not -path '*/.git/*' 2>/dev/null

For multi-file operations (stack, mosaic), if a directory is given:

find "INPUT_DIR" -name "*.tif" -o -name "*.tiff" 2>/dev/null | sort

If the user recently inspected or downloaded a file and did not specify an input, check the state file for context:

STATE_DIR=""
test -f .geoai-skills/state.json && STATE_DIR=".geoai-skills"
PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
PROJECT_ID="$(echo "$PROJECT_ROOT" | tr '/' '-')"
test -f "$HOME/.geoai-skills/$PROJECT_ID/state.json" && STATE_DIR="$HOME/.geoai-skills/$PROJECT_ID"

If state exists, read the last inspected or downloaded file:

python3 -c "
import json
with open('STATE_DIR/state.json') as f:
    state = json.load(f)
if 'last_inspected' in state:
    print(f'Last inspected: {state[\"last_inspected\"][\"path\"]}')
if 'downloaded_files' in state:
    for f in state['downloaded_files']:
        print(f'Downloaded: {f}')
"

Step 3 -- Execute the operation

Clip by bounding box

python3 -c "
import geoai

result = geoai.clip_raster_by_bbox(
    input_raster='INPUT_PATH',
    output_raster='OUTPUT_PATH',
    bbox=[MINX, MINY, MAXX, MAXY],
)
print(f'Clipped raster saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Default output: ./clipped_<original_name>.tif

Stack bands

python3 -c "
import geoai

result = geoai.stack_bands(
    input_files=['FILE1', 'FILE2', 'FILE3'],
    output_file='OUTPUT_PATH',
)
print(f'Stacked raster saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Mosaic GeoTIFFs

python3 -c "
import geoai

result = geoai.mosaic_geotiffs(
    input_dir='INPUT_DIR',
    output_file='OUTPUT_PATH',
)
print(f'Mosaic saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Raster to vector

python3 -c "
import geoai

gdf = geoai.raster_to_vector(
    raster_path='INPUT_PATH',
    output_path='OUTPUT_PATH',
)
print(f'Vectorized: {len(gdf)} features')
print(f'Saved to: OUTPUT_PATH')
print(f'Columns: {list(gdf.columns)}')
"

Default output: ./<original_name>.gpkg

Vector to raster

python3 -c "
import geoai

result = geoai.vector_to_raster(
    vector_path='INPUT_PATH',
    output_path='OUTPUT_PATH',
    pixel_size=PIXEL_SIZE,
)
print(f'Rasterized: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Default pixel size: 1.0 (or infer from context). Default output: ./<original_name>.tif

Replace all placeholder values with actual paths and parameters before running.

Step 4 -- Update state

If a state directory exists, update it with the output file path using the same state resolution pattern as Step 2.

Step 5 -- Report and suggest

Report:

• Operation performed
• Input and output file paths
• Key properties of the output (dimensions, CRS, band count, feature count)

Then suggest: "Use /geoai-skills:inspect-geo to examine the result in detail."

Error handling

• import geoai fails -> delegate to /geoai-skills:install-geoai.
• File not found -> use find to locate, suggest corrected path.
• CRS mismatch (for stack/mosaic) -> report the issue and suggest reprojecting first.
• Insufficient disk space -> report the error.
• Memory error (very large rasters) -> suggest processing in tiles or using a smaller extent.