Solar Panel Detection¶

This notebook demonstrates how to use the geoai package for solar panel detection using a pre-trained model.

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/solar_panels_davis_ca.tif"
raster_path = geoai.download_file(raster_url)

raster_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/solar_panels_davis_ca.tif" raster_path = geoai.download_file(raster_url)

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geoai.print_raster_info(raster_path)

geoai.print_raster_info(raster_path)

Visualize data¶

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geoai.view_raster(raster_url)

geoai.view_raster(raster_url)

Initialize model¶

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detector = geoai.SolarPanelDetector()

detector = geoai.SolarPanelDetector()

Generate masks¶

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output_path = "solar_panel_masks.tif"

output_path = "solar_panel_masks.tif"

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masks_path = detector.generate_masks(
    raster_path,
    output_path=output_path,
    confidence_threshold=0.4,
    mask_threshold=0.5,
    min_object_area=100,
    overlap=0.25,
    chip_size=(400, 400),
    batch_size=4,
    verbose=False,
)

masks_path = detector.generate_masks( raster_path, output_path=output_path, confidence_threshold=0.4, mask_threshold=0.5, min_object_area=100, overlap=0.25, chip_size=(400, 400), batch_size=4, verbose=False, )

Visualize masks¶

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geoai.view_raster(
    output_path,
    indexes=[2],
    colormap="autumn",
    layer_name="Solar Panels",
    basemap=raster_url,
)

geoai.view_raster( output_path, indexes=[2], colormap="autumn", layer_name="Solar Panels", basemap=raster_url, )

Vectorize masks¶

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gdf = geoai.orthogonalize(
    input_path=masks_path, output_path="solar_panel_masks.geojson", epsilon=0.2
)

gdf = geoai.orthogonalize( input_path=masks_path, output_path="solar_panel_masks.geojson", epsilon=0.2 )

Visualize initial results¶

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geoai.view_vector_interactive(gdf, tiles=raster_url)

geoai.view_vector_interactive(gdf, tiles=raster_url)

Calculate geometric properties¶

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gdf = geoai.add_geometric_properties(gdf)
gdf.head()

gdf = geoai.add_geometric_properties(gdf) gdf.head()

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print(len(gdf))

print(len(gdf))

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geoai.view_vector_interactive(gdf, column="elongation", tiles=raster_url)

geoai.view_vector_interactive(gdf, column="elongation", tiles=raster_url)

Filter results¶

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gdf_filter = gdf[(gdf["elongation"] < 10) & (gdf["area_m2"] > 5)]
print(len(gdf_filter))

gdf_filter = gdf[(gdf["elongation"] < 10) & (gdf["area_m2"] > 5)] print(len(gdf_filter))

Visualize final results¶

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geoai.view_vector_interactive(gdf_filter, column="area_m2", tiles=raster_url)

geoai.view_vector_interactive(gdf_filter, column="area_m2", tiles=raster_url)

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geoai.view_vector_interactive(
    gdf_filter, style_kwds={"color": "red", "fillOpacity": 0}, tiles=raster_url
)

geoai.view_vector_interactive( gdf_filter, style_kwds={"color": "red", "fillOpacity": 0}, tiles=raster_url )

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gdf_filter["area_m2"].hist()

gdf_filter["area_m2"].hist()

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gdf_filter["area_m2"].describe()

gdf_filter["area_m2"].describe()

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gdf_filter["area_m2"].sum()

gdf_filter["area_m2"].sum()

Save results¶

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gdf_filter.to_file("solar_panels.geojson")

gdf_filter.to_file("solar_panels.geojson")