Quickstart

Quickstart#

This page grades every building in a drone orthomosaic of Altadena, CA, captured after the 2025 Eaton Fire, and writes the grades to GeoJSON. It runs fully offline with a local Gemma 4 model, and needs a GPU with a few gigabytes of memory.

The whole workflow#

from pathlib import Path
import rapidtools as rt

rt.configure_logging()

# 1. Sample data: an orthomosaic, building footprints and a prompt file.
raster, footprints, prompt = rt.download_dataset(
    ['eaton_patch2', 'altadena_sample_buildings', 'aerial_chs_prompts']
)

# 2. Load the footprints into a collection of assets.
buildings = rt.PhysicalAssetCollection.from_geojson(footprints)

# 3. Choose a model. Swap 'gemma4' for 'gemini', 'claude', 'openai', ...
model = rt.models.load('gemma4', model_id='google/gemma-4-E2B-it')

# 4. Build the pipeline: crop each footprint, then ask the model about it.
pipeline = rt.Pipeline([
    rt.AerialImageryExtractor(
        raster,
        save_directory=Path('output/crops'),
        buffer_m=5,
        overlay_asset_outline=True,
    ),
    rt.AssetAnalyzer(model, prompt=prompt, batch_size=8),
])
buildings = pipeline.run(buildings)

# 5. Export. Model answers are attributes prefixed with the provider name.
buildings.filter_empty().to_geojson(
    'output/buildings_graded.geojson', ignore_properties=['image_assets']
)

What happened#

  1. AerialImageryExtractor read the raster once, cropped a square patch around each footprint with a 5 m margin, drew the footprint outline on the patch so the model knows which building to look at, and attached the JPEG to the asset as an ImageAsset.

  2. AssetAnalyzer sent each asset’s crops and the prompt to the model, eight assets per batch, and stored the answer on the asset. The prompt file asks for a JSON object, so each key becomes an attribute such as gemma4_chs_level.

  3. to_geojson() wrote the footprints with their new attributes; ignore_properties keeps the image paths out of the file.

Using a hosted model instead#

Only step 3 changes. Hosted models run several requests in parallel and back off automatically when the provider rate-limits them:

model = rt.models.load('gemini', api_key='AIza...', model_id='gemini-3.8-flash')
analyzer = rt.AssetAnalyzer(model, prompt=prompt, max_workers=4)

Reading the results#

>>> asset = buildings.get('bldg_042')
>>> asset.attributes['gemma4_chs_level']
'3'
>>> asset.image_assets[0].path
PosixPath('/.../output/crops/aerial_3418715171_-11814335588.jpg')
>>> buildings.to_dataframe().groupby('gemma4_chs_level').size()

Next steps#