Building Footprint Extraction for the USA¶

Install package¶

To use the geoai-py package, ensure it is installed in your environment. Uncomment the command below if needed.

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# %pip install geoai-py

# %pip install geoai-py

Import libraries¶

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import geoai

import geoai

Download sample data¶

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raster_url = (
    "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif"
)
vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"

raster_url = ( "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif" ) vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"

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raster_path = geoai.download_file(raster_url)

raster_path = geoai.download_file(raster_url)

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vector_path = geoai.download_file(vector_url)

vector_path = geoai.download_file(vector_url)

Initialize building footprint extraction pretrained model¶

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extractor = geoai.BuildingFootprintExtractor()

extractor = geoai.BuildingFootprintExtractor()

Extract building footprints¶

Option 1: Extract building footprints as raster¶

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mask_path = extractor.save_masks_as_geotiff(
    raster_path=raster_path,
    output_path="building_masks.tif",
    confidence_threshold=0.5,
    mask_threshold=0.5,
)

mask_path = extractor.save_masks_as_geotiff( raster_path=raster_path, output_path="building_masks.tif", confidence_threshold=0.5, mask_threshold=0.5, )

Convert raster to vector

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gdf = extractor.masks_to_vector(
    mask_path=mask_path,
    output_path="building_masks.geojson",
    simplify_tolerance=1.0,
)

gdf = extractor.masks_to_vector( mask_path=mask_path, output_path="building_masks.geojson", simplify_tolerance=1.0, )

Option 2: Extract building footprints as vector¶

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output_path = "naip_buildings.geojson"
gdf = extractor.process_raster(
    raster_path,
    output_path="buildings.geojson",
    batch_size=4,
    confidence_threshold=0.5,
    overlap=0.25,
    nms_iou_threshold=0.5,
    min_object_area=100,
    max_object_area=None,
    mask_threshold=0.5,
    simplify_tolerance=1.0,
)

output_path = "naip_buildings.geojson" gdf = extractor.process_raster( raster_path, output_path="buildings.geojson", batch_size=4, confidence_threshold=0.5, overlap=0.25, nms_iou_threshold=0.5, min_object_area=100, max_object_area=None, mask_threshold=0.5, simplify_tolerance=1.0, )

Regularize building footprints¶

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gdf_regularized = extractor.regularize_buildings(
    gdf=gdf,
    min_area=100,
    angle_threshold=15,
    orthogonality_threshold=0.3,
    rectangularity_threshold=0.7,
)

gdf_regularized = extractor.regularize_buildings( gdf=gdf, min_area=100, angle_threshold=15, orthogonality_threshold=0.3, rectangularity_threshold=0.7, )

Visualize building footprints¶

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gdf.head()

gdf.head()

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geoai.view_vector_interactive(
    gdf, column="confidence", layer_name="Building", tiles="Satellite"
)

geoai.view_vector_interactive( gdf, column="confidence", layer_name="Building", tiles="Satellite" )

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geoai.view_vector_interactive(
    gdf, column="confidence", layer_name="Building", tiles=raster_url
)

geoai.view_vector_interactive( gdf, column="confidence", layer_name="Building", tiles=raster_url )

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geoai.view_vector_interactive(
    gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url
)

geoai.view_vector_interactive( gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url )

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extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")

extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")

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extractor.visualize_results(
    raster_path, gdf_regularized, output_path="naip_buildings_regularized.png"
)

extractor.visualize_results( raster_path, gdf_regularized, output_path="naip_buildings_regularized.png" )