Train a Model for Detecting Surface Water¶

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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train_raster_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_train.tif"
train_masks_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_masks.tif"
test_raster_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_test.tif"

train_raster_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_train.tif" train_masks_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_masks.tif" test_raster_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip/naip_water_test.tif"

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train_raster_path = geoai.download_file(train_raster_url)
train_masks_path = geoai.download_file(train_masks_url)
test_raster_path = geoai.download_file(test_raster_url)

train_raster_path = geoai.download_file(train_raster_url) train_masks_path = geoai.download_file(train_masks_url) test_raster_path = geoai.download_file(test_raster_url)

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geoai.print_raster_info(train_raster_path, show_preview=False)

geoai.print_raster_info(train_raster_path, show_preview=False)

Visualize sample data¶

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geoai.view_raster(train_masks_url, nodata=0, basemap=train_raster_url)

geoai.view_raster(train_masks_url, nodata=0, basemap=train_raster_url)

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geoai.view_raster(test_raster_url)

geoai.view_raster(test_raster_url)

Create training data¶

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out_folder = "output"

out_folder = "output"

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tiles = geoai.export_geotiff_tiles(
    in_raster=train_raster_path,
    out_folder=out_folder,
    in_class_data=train_masks_path,
    tile_size=512,
    stride=128,
    buffer_radius=0,
)

tiles = geoai.export_geotiff_tiles( in_raster=train_raster_path, out_folder=out_folder, in_class_data=train_masks_path, tile_size=512, stride=128, buffer_radius=0, )

Train object detection model¶

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geoai.train_MaskRCNN_model(
    images_dir=f"{out_folder}/images",
    labels_dir=f"{out_folder}/labels",
    output_dir=f"{out_folder}/models",
    num_channels=4,
    pretrained=True,
    batch_size=4,
    num_epochs=10,
    learning_rate=0.005,
    val_split=0.2,
)

geoai.train_MaskRCNN_model( images_dir=f"{out_folder}/images", labels_dir=f"{out_folder}/labels", output_dir=f"{out_folder}/models", num_channels=4, pretrained=True, batch_size=4, num_epochs=10, learning_rate=0.005, val_split=0.2, )

Run inference¶

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masks_path = "naip_water_prediction.tif"
model_path = f"{out_folder}/models/best_model.pth"

masks_path = "naip_water_prediction.tif" model_path = f"{out_folder}/models/best_model.pth"

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geoai.object_detection(
    test_raster_path,
    masks_path,
    model_path,
    window_size=512,
    overlap=128,
    confidence_threshold=0.3,
    batch_size=4,
    num_channels=4,
)

geoai.object_detection( test_raster_path, masks_path, model_path, window_size=512, overlap=128, confidence_threshold=0.3, batch_size=4, num_channels=4, )

Vectorize masks¶

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output_path = "naip_water_prediction.geojson"
gdf = geoai.raster_to_vector(
    masks_path, output_path, min_area=1000, simplify_tolerance=1
)

output_path = "naip_water_prediction.geojson" gdf = geoai.raster_to_vector( masks_path, output_path, min_area=1000, simplify_tolerance=1 )

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gdf = geoai.add_geometric_properties(gdf)

gdf = geoai.add_geometric_properties(gdf)

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len(gdf)

len(gdf)

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geoai.view_vector_interactive(gdf, tiles=test_raster_url)

geoai.view_vector_interactive(gdf, tiles=test_raster_url)

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gdf["elongation"].hist()

gdf["elongation"].hist()

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gdf_filtered = gdf[gdf["elongation"] < 10]

gdf_filtered = gdf[gdf["elongation"] < 10]

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len(gdf_filtered)

len(gdf_filtered)

Visualize results¶

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geoai.view_vector_interactive(gdf_filtered, tiles=test_raster_url)

geoai.view_vector_interactive(gdf_filtered, tiles=test_raster_url)

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geoai.create_split_map(
    left_layer=gdf_filtered,
    right_layer=test_raster_url,
    left_args={"style": {"color": "red", "fillOpacity": 0.2}},
    basemap=test_raster_url,
)

geoai.create_split_map( left_layer=gdf_filtered, right_layer=test_raster_url, left_args={"style": {"color": "red", "fillOpacity": 0.2}}, basemap=test_raster_url, )