Download Data from Planetary Computer¶

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

Retrieve collections¶

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collections = geoai.pc_collection_list()
collections

collections = geoai.pc_collection_list() collections

Search NAIP imagery¶

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items = geoai.pc_stac_search(
    collection="naip",
    bbox=[-76.6657, 39.2648, -76.6478, 39.2724],  # Baltimore area
    time_range="2013-01-01/2014-12-31",
)

items = geoai.pc_stac_search( collection="naip", bbox=[-76.6657, 39.2648, -76.6478, 39.2724], # Baltimore area time_range="2013-01-01/2014-12-31", )

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items

items

Visualize NAIP imagery¶

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geoai.pc_item_asset_list(items[0])

geoai.pc_item_asset_list(items[0])

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geoai.view_pc_item(item=items[0])

geoai.view_pc_item(item=items[0])

Download NAIP imagery¶

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downloaded = geoai.pc_stac_download(
    items, output_dir="data", assets=["image", "thumbnail"]
)

downloaded = geoai.pc_stac_download( items, output_dir="data", assets=["image", "thumbnail"] )

Search land cover data¶

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items = geoai.pc_stac_search(
    collection="chesapeake-lc-13",
    bbox=[-76.6657, 39.2648, -76.6478, 39.2724],  # Baltimore area
    time_range="2013-01-01/2014-12-31",
    max_items=10,
)

items = geoai.pc_stac_search( collection="chesapeake-lc-13", bbox=[-76.6657, 39.2648, -76.6478, 39.2724], # Baltimore area time_range="2013-01-01/2014-12-31", max_items=10, )

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items

items

Visualize land cover data¶

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geoai.pc_item_asset_list(items[0])

geoai.pc_item_asset_list(items[0])

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geoai.view_pc_item(item=items[0], colormap_name="tab10", basemap="SATELLITE")

geoai.view_pc_item(item=items[0], colormap_name="tab10", basemap="SATELLITE")

Download land cover data¶

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geoai.pc_stac_download(items[0], output_dir="data", assets=["data", "rendered_preview"])

geoai.pc_stac_download(items[0], output_dir="data", assets=["data", "rendered_preview"])

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ds = geoai.read_pc_item_asset(items[0], asset="data")

ds = geoai.read_pc_item_asset(items[0], asset="data")

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ds

ds

Search Landsat data¶

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items = geoai.pc_stac_search(
    collection="landsat-c2-l2",
    bbox=[-76.6657, 39.2648, -76.6478, 39.2724],  # Baltimore area
    time_range="2024-10-27/2024-12-31",
    query={"eo:cloud_cover": {"lt": 1}},
    max_items=10,
)

items = geoai.pc_stac_search( collection="landsat-c2-l2", bbox=[-76.6657, 39.2648, -76.6478, 39.2724], # Baltimore area time_range="2024-10-27/2024-12-31", query={"eo:cloud_cover": {"lt": 1}}, max_items=10, )

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items

items

Visualize Landsat data¶

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geoai.pc_item_asset_list(items[0])

geoai.pc_item_asset_list(items[0])

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geoai.view_pc_item(item=items[0], assets=["red", "green", "blue"])

geoai.view_pc_item(item=items[0], assets=["red", "green", "blue"])

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geoai.view_pc_item(item=items[0], assets=["nir08", "red", "green"])

geoai.view_pc_item(item=items[0], assets=["nir08", "red", "green"])

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geoai.view_pc_item(
    item=items[0],
    expression="(nir08-red)/(nir08+red)",
    rescale="-1,1",
    colormap_name="greens",
    name="NDVI Green",
)

geoai.view_pc_item( item=items[0], expression="(nir08-red)/(nir08+red)", rescale="-1,1", colormap_name="greens", name="NDVI Green", )

Download Landsat data¶

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geoai.pc_stac_download(
    items[0], output_dir="data", assets=["nir08", "red", "green", "blue"]
)

geoai.pc_stac_download( items[0], output_dir="data", assets=["nir08", "red", "green", "blue"] )