Uncomment the following line to install geemap if needed.

In [ ]:

# !pip install geemap

In [ ]:

import ee
import geemap
import os

In [ ]:

geemap.show_youtube('_6JOA-iiEGU')

In [ ]:

Map = geemap.Map()
Map

Download an ee.Image¶

In [ ]:

image = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(image, landsat_vis, "LE7_TOA_5YEAR/1999_2003", True, 0.7)

In [ ]:

# Draw any shapes on the map using the Drawing tools before executing this code block
feature = Map.draw_last_feature

if feature is None:
    geom = ee.Geometry.Polygon(
        [
            [
                [-115.413031, 35.889467],
                [-115.413031, 36.543157],
                [-114.034328, 36.543157],
                [-114.034328, 35.889467],
                [-115.413031, 35.889467],
            ]
        ]
    )
    feature = ee.Feature(geom, {})

roi = feature.geometry()

In [ ]:

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')
filename = os.path.join(out_dir, 'landsat.tif')

Exporting all bands as one single image¶

In [ ]:

image = image.clip(roi).unmask()
geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=False
)

Exporting each band as one image¶

In [ ]:

geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=True
)

Export an image to Google Drive¶

In [ ]:

geemap.ee_export_image_to_drive(
    image, description='landsat', folder='export', region=roi, scale=30
)

Download an ee.ImageCollection¶

In [ ]:

import ee
import geemap
import os

In [ ]:

loc = ee.Geometry.Point(-99.2222, 46.7816)
collection = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(loc)
    .filterDate('2008-01-01', '2020-01-01')
    .filter(ee.Filter.listContains("system:band_names", "N"))
)

In [ ]:

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')

In [ ]:

print(collection.aggregate_array('system:index').getInfo())

In [ ]:

geemap.ee_export_image_collection(collection, out_dir=out_dir)

In [ ]:

geemap.ee_export_image_collection_to_drive(collection, folder='export', scale=10)

Extract pixels as a Numpy array¶

In [ ]:

import ee
import geemap
import numpy as np
import matplotlib.pyplot as plt

img = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810').select(['B4', 'B5', 'B6'])

aoi = ee.Geometry.Polygon(
    [[[-110.8, 44.7], [-110.8, 44.6], [-110.6, 44.6], [-110.6, 44.7]]], None, False
)

rgb_img = geemap.ee_to_numpy(img, region=aoi)
print(rgb_img.shape)

In [ ]:

# Scale the data to [0, 255] to show as an RGB image.
# Adapted from https://bit.ly/2XlmQY8. Credits to Justin Braaten
rgb_img_test = (255 * ((rgb_img[:, :, 0:3] - 100) / 3500)).astype('uint8')
plt.imshow(rgb_img_test)
plt.show()