Globe projection

Visualize geospatial data on a 3D globe

Uncomment the following line to install geoai if needed.

In [ ]:

# %pip install geoai-py

# %pip install geoai-py

Import library¶

In [ ]:

import geoai

import geoai

Add globe control¶

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m = geoai.MapLibre(center=[-100, 40], zoom=3, style="liberty")
m.add_globe_control()
m

m = geoai.MapLibre(center=[-100, 40], zoom=3, style="liberty") m.add_globe_control() m

Use globe projection¶

In [ ]:

m = geoai.MapLibre(center=[-100, 40], zoom=3, style="positron", projection="globe")
m.add_basemap("Esri.WorldImagery")
m.add_overture_3d_buildings()
m

m = geoai.MapLibre(center=[-100, 40], zoom=3, style="positron", projection="globe") m.add_basemap("Esri.WorldImagery") m.add_overture_3d_buildings() m

Create 3D choropleth maps¶

In [ ]:

m = geoai.MapLibre(
    center=[19.43, 49.49], zoom=3, pitch=60, style="positron", projection="globe"
)
source = {
    "type": "geojson",
    "data": "https://docs.maptiler.com/sdk-js/assets/Mean_age_of_women_at_first_marriage_in_2019.geojson",
}
m.add_source("countries", source)
layer = {
    "id": "eu-countries",
    "source": "countries",
    "type": "fill-extrusion",
    "paint": {
        "fill-extrusion-color": [
            "interpolate",
            ["linear"],
            ["get", "age"],
            23.0,
            "#fff5eb",
            24.0,
            "#fee6ce",
            25.0,
            "#fdd0a2",
            26.0,
            "#fdae6b",
            27.0,
            "#fd8d3c",
            28.0,
            "#f16913",
            29.0,
            "#d94801",
            30.0,
            "#8c2d04",
        ],
        "fill-extrusion-opacity": 1,
        "fill-extrusion-height": ["*", ["get", "age"], 5000],
    },
}
first_symbol_layer_id = m.find_first_symbol_layer()["id"]
m.add_layer(layer, first_symbol_layer_id)
m.add_layer_control()
m

m = geoai.MapLibre( center=[19.43, 49.49], zoom=3, pitch=60, style="positron", projection="globe" ) source = { "type": "geojson", "data": "https://docs.maptiler.com/sdk-js/assets/Mean_age_of_women_at_first_marriage_in_2019.geojson", } m.add_source("countries", source) layer = { "id": "eu-countries", "source": "countries", "type": "fill-extrusion", "paint": { "fill-extrusion-color": [ "interpolate", ["linear"], ["get", "age"], 23.0, "#fff5eb", 24.0, "#fee6ce", 25.0, "#fdd0a2", 26.0, "#fdae6b", 27.0, "#fd8d3c", 28.0, "#f16913", 29.0, "#d94801", 30.0, "#8c2d04", ], "fill-extrusion-opacity": 1, "fill-extrusion-height": ["*", ["get", "age"], 5000], }, } first_symbol_layer_id = m.find_first_symbol_layer()["id"] m.add_layer(layer, first_symbol_layer_id) m.add_layer_control() m

In [ ]:

data = "https://github.com/opengeos/datasets/releases/download/vector/countries.geojson"

data = "https://github.com/opengeos/datasets/releases/download/vector/countries.geojson"

In [ ]:

m = geoai.MapLibre(style="liberty", projection="globe")
first_symbol_id = m.find_first_symbol_layer()["id"]
m.add_data(
    data,
    column="POP_EST",
    scheme="Quantiles",
    cmap="Blues",
    legend_title="Population",
    name="Population",
    before_id=first_symbol_id,
    extrude=True,
    scale_factor=1000,
)
m.add_layer_control()
m

m = geoai.MapLibre(style="liberty", projection="globe") first_symbol_id = m.find_first_symbol_layer()["id"] m.add_data( data, column="POP_EST", scheme="Quantiles", cmap="Blues", legend_title="Population", name="Population", before_id=first_symbol_id, extrude=True, scale_factor=1000, ) m.add_layer_control() m

Vector data¶

In [ ]:

m = geoai.MapLibre(style="liberty", projection="globe")
train_raster_url = (
    "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_rgb_train.tif"
)
train_vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"
m.add_cog_layer(train_raster_url, name="NAIP")
paint = {"fill-color": "#ff0000", "fill-opacity": 0.4, "fill-outline-color": "#ffff00"}
m.add_geojson(train_vector_url, name="Buildings", layer_type="fill", paint=paint)
m.add_layer_control()
m

m = geoai.MapLibre(style="liberty", projection="globe") train_raster_url = ( "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_rgb_train.tif" ) train_vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson" m.add_cog_layer(train_raster_url, name="NAIP") paint = {"fill-color": "#ff0000", "fill-opacity": 0.4, "fill-outline-color": "#ffff00"} m.add_geojson(train_vector_url, name="Buildings", layer_type="fill", paint=paint) m.add_layer_control() m

Planetary Computer¶

In [ ]:

collection = "landsat-8-c2-l2"
item = "LC08_L2SP_047027_20201204_02_T1"

collection = "landsat-8-c2-l2" item = "LC08_L2SP_047027_20201204_02_T1"

In [ ]:

m = geoai.MapLibre(projection="globe")
m.add_stac_layer(
    collection=collection, item=item, assets="SR_B7,SR_B5,SR_B4", name="False color"
)
m

m = geoai.MapLibre(projection="globe") m.add_stac_layer( collection=collection, item=item, assets="SR_B7,SR_B5,SR_B4", name="False color" ) m

In [ ]:

m = geoai.MapLibre(projection="globe")
m.add_stac_layer(
    collection=collection,
    item=item,
    assets=["SR_B5", "SR_B4", "SR_B3"],
    name="Color infrared",
)
m

m = geoai.MapLibre(projection="globe") m.add_stac_layer( collection=collection, item=item, assets=["SR_B5", "SR_B4", "SR_B3"], name="Color infrared", ) m

In [ ]:

m = geoai.MapLibre(projection="globe")
m.add_stac_layer(
    collection=collection, item=item, assets="SR_B5,SR_B4,SR_B3", name="Color infrared"
)
m.add_stac_layer(
    collection=collection,
    item=item,
    expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)",
    rescale="-1,1",
    colormap_name="greens",
    name="NDVI Green",
)
m

m = geoai.MapLibre(projection="globe") m.add_stac_layer( collection=collection, item=item, assets="SR_B5,SR_B4,SR_B3", name="Color infrared" ) m.add_stac_layer( collection=collection, item=item, expression="(SR_B5-SR_B4)/(SR_B5+SR_B4)", rescale="-1,1", colormap_name="greens", name="NDVI Green", ) m