24 publish maps

Uncomment the following line to install geemap if needed.

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# !pip install geemap

# !pip install geemap

To follow this tutorial, you will need to sign up for an account with https://datapane.com, then install and authenticate the datapane Python package. More information can be found here.

• pip install datapane
• datapane login
• datapane ping

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import ee
import geemap.foliumap as geemap

import ee import geemap.foliumap as geemap

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# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)

# Use an elevation dataset and terrain functions to create
# a custom visualization of topography.

# Load a global elevation image.
elev = ee.Image('USGS/GMTED2010')

# Zoom to an area of interest.
Map.setCenter(-121.069, 50.709, 6)

# Add the elevation to the map.
Map.addLayer(elev, {}, 'elev')

# Use the terrain algorithms to compute a hillshade with 8-bit values.
shade = ee.Terrain.hillshade(elev)
Map.addLayer(shade, {}, 'hillshade', False)

# Create a "sea" variable to be used for cartographic purposes
sea = elev.lte(0)
Map.addLayer(sea.mask(sea), {'palette': '000022'}, 'sea', False)

# Create a custom elevation palette from hex strings.
elevationPalette = ['006600', '002200', 'fff700', 'ab7634', 'c4d0ff', 'ffffff']
# Use these visualization parameters, customized by location.
visParams = {'min': 1, 'max': 3000, 'palette': elevationPalette}

# Create a mosaic of the sea and the elevation data
visualized = ee.ImageCollection(
    [
        # Mask the elevation to get only land
        elev.mask(sea.Not()).visualize(**visParams),
        # Use the sea mask directly to display sea.
        sea.mask(sea).visualize(**{'palette': '000022'}),
    ]
).mosaic()

# Note that the visualization image doesn't require visualization parameters.
Map.addLayer(visualized, {}, 'elev palette', False)

# Convert the visualized elevation to HSV, first converting to [0, 1] data.
hsv = visualized.divide(255).rgbToHsv()
# Select only the hue and saturation bands.
hs = hsv.select(0, 1)
# Convert the hillshade to [0, 1] data, as expected by the HSV algorithm.
v = shade.divide(255)
# Create a visualization image by converting back to RGB from HSV.
# Note the cast to byte in order to export the image correctly.
rgb = hs.addBands(v).hsvToRgb().multiply(255).byte()
Map.addLayer(rgb, {}, 'styled')

states = ee.FeatureCollection('TIGER/2018/States')
Map.addLayer(ee.Image().paint(states, 0, 2), {}, "US States")

# Create a map centered at (lat, lon). Map = geemap.Map(center=[40, -100], zoom=4) # Use an elevation dataset and terrain functions to create # a custom visualization of topography. # Load a global elevation image. elev = ee.Image('USGS/GMTED2010') # Zoom to an area of interest. Map.setCenter(-121.069, 50.709, 6) # Add the elevation to the map. Map.addLayer(elev, {}, 'elev') # Use the terrain algorithms to compute a hillshade with 8-bit values. shade = ee.Terrain.hillshade(elev) Map.addLayer(shade, {}, 'hillshade', False) # Create a "sea" variable to be used for cartographic purposes sea = elev.lte(0) Map.addLayer(sea.mask(sea), {'palette': '000022'}, 'sea', False) # Create a custom elevation palette from hex strings. elevationPalette = ['006600', '002200', 'fff700', 'ab7634', 'c4d0ff', 'ffffff'] # Use these visualization parameters, customized by location. visParams = {'min': 1, 'max': 3000, 'palette': elevationPalette} # Create a mosaic of the sea and the elevation data visualized = ee.ImageCollection( [ # Mask the elevation to get only land elev.mask(sea.Not()).visualize(**visParams), # Use the sea mask directly to display sea. sea.mask(sea).visualize(**{'palette': '000022'}), ] ).mosaic() # Note that the visualization image doesn't require visualization parameters. Map.addLayer(visualized, {}, 'elev palette', False) # Convert the visualized elevation to HSV, first converting to [0, 1] data. hsv = visualized.divide(255).rgbToHsv() # Select only the hue and saturation bands. hs = hsv.select(0, 1) # Convert the hillshade to [0, 1] data, as expected by the HSV algorithm. v = shade.divide(255) # Create a visualization image by converting back to RGB from HSV. # Note the cast to byte in order to export the image correctly. rgb = hs.addBands(v).hsvToRgb().multiply(255).byte() Map.addLayer(rgb, {}, 'styled') states = ee.FeatureCollection('TIGER/2018/States') Map.addLayer(ee.Image().paint(states, 0, 2), {}, "US States")

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# Display the map.
Map

# Display the map. Map

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Map.publish(
    name='Terrain Visualization',
    description='A folium map with Earth Engine data layers',
)

Map.publish( name='Terrain Visualization', description='A folium map with Earth Engine data layers', )

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Last update: 2023-03-19
Created: 2020-07-05