diff --git a/examples/AAFC/AAFC_ACI.py b/examples/AAFC/AAFC_ACI.py
new file mode 100644
index 000000000..610ef0fa9
--- /dev/null
+++ b/examples/AAFC/AAFC_ACI.py
@@ -0,0 +1,7 @@
+dataset = ee.ImageCollection('AAFC/ACI')
+crop_2016 = dataset.filter(ee.Filter.date('2016-01-01', '2016-12-31')).first()
+
+m = geemap.Map()
+m.set_center(-103.8881, 53.0372, 10)
+m.add_layer(crop_2016, {}, '2016 Canada AAFC Annual Crop Inventory')
+m
diff --git a/examples/AHN/AHN_AHN2_05M_INT.py b/examples/AHN/AHN_AHN2_05M_INT.py
new file mode 100644
index 000000000..3bebd12ec
--- /dev/null
+++ b/examples/AHN/AHN_AHN2_05M_INT.py
@@ -0,0 +1,8 @@
+dataset = ee.Image('AHN/AHN2_05M_INT')
+elevation = dataset.select('elevation')
+elevation_vis = {'min': -5.0, 'max': 30.0}
+
+m = geemap.Map()
+m.set_center(5.76583, 51.855276, 16)
+m.add_layer(elevation, elevation_vis, 'Elevation')
+m
diff --git a/examples/ASTER/ASTER_AST_L1T_003.py b/examples/ASTER/ASTER_AST_L1T_003.py
new file mode 100644
index 000000000..81a08b65e
--- /dev/null
+++ b/examples/ASTER/ASTER_AST_L1T_003.py
@@ -0,0 +1,11 @@
+dataset = ee.ImageCollection('ASTER/AST_L1T_003').filter(
+    ee.Filter.date('2018-01-01', '2018-08-15')
+)
+
+false_color = dataset.select(['B3N', 'B02', 'B01'])
+false_color_vis = {'min': 0.0, 'max': 255.0}
+
+m = geemap.Map()
+m.set_center(-122.0272, 39.6734, 11)
+m.add_layer(false_color.median(), false_color_vis, 'False Color')
+m
diff --git a/examples/BIOPAMA/BIOPAMA_GlobalOilPalm_v1.py b/examples/BIOPAMA/BIOPAMA_GlobalOilPalm_v1.py
new file mode 100644
index 000000000..62a3e8ef9
--- /dev/null
+++ b/examples/BIOPAMA/BIOPAMA_GlobalOilPalm_v1.py
@@ -0,0 +1,30 @@
+# Import the dataset a collection of composite granules from 2019.
+dataset = ee.ImageCollection('BIOPAMA/GlobalOilPalm/v1')
+
+# Select the classification band.
+op_class = dataset.select('classification')
+
+# Mosaic all of the granules into a single image.
+mosaic = op_class.mosaic()
+
+# Define visualization parameters.
+classification_vis = {
+    'min': 1,
+    'max': 3,
+    'palette': ['ff0000', 'ef00ff', '696969'],
+}
+
+# Create a mask to add transparency to non-oil palm plantation class pixels.
+mask = mosaic.neq(3)
+mask = mask.where(mask.eq(0), 0.6)
+
+# Display the data on the map.
+m = geemap.Map()
+m.add_layer(
+    mosaic.updateMask(mask),
+    classification_vis,
+    'Oil palm plantation type',
+    True,
+)
+m.set_center(-3.0175, 5.2745, 12)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_CORINE_V20_100m.py b/examples/COPERNICUS/COPERNICUS_CORINE_V20_100m.py
new file mode 100644
index 000000000..12ad85ecd
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_CORINE_V20_100m.py
@@ -0,0 +1,7 @@
+dataset = ee.Image('COPERNICUS/CORINE/V20/100m/2012')
+land_cover = dataset.select('landcover')
+
+m = geemap.Map()
+m.set_center(16.436, 39.825, 6)
+m.add_layer(land_cover, {}, 'Land Cover')
+m
diff --git a/examples/COPERNICUS/COPERNICUS_DEM_GLO30.py b/examples/COPERNICUS/COPERNICUS_DEM_GLO30.py
new file mode 100644
index 000000000..73b99db7e
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_DEM_GLO30.py
@@ -0,0 +1,13 @@
+dataset = ee.ImageCollection('COPERNICUS/DEM/GLO30')
+elevation = dataset.select('DEM')
+
+elevation_vis = {
+    'min': 0.0,
+    'max': 1000.0,
+    'palette': ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff'],
+}
+
+m = geemap.Map()
+m.set_center(-6.746, 46.529, 4)
+m.add_layer(elevation, elevation_vis, 'DEM')
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S2.py b/examples/COPERNICUS/COPERNICUS_S2.py
new file mode 100644
index 000000000..3412dcaa7
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S2.py
@@ -0,0 +1,41 @@
+def mask_s2_clouds(image):
+  """Masks clouds using the Sentinel-2 QA band.
+
+  Args:
+      image (ee.Image): Sentinel-2 image.
+
+  Returns:
+      ee.Image: Cloud masked Sentinel-2 image.
+  """
+  qa = image.select('QA60')
+
+  # Bits 10 and 11 are clouds and cirrus, respectively.
+  cloud_bit_mask = 1 << 10
+  cirrus_bit_mask = 1 << 11
+
+  # Both flags should be set to zero, indicating clear conditions.
+  mask = (
+      qa.bitwiseAnd(cloud_bit_mask)
+      .eq(0)
+      .And(qa.bitwiseAnd(cirrus_bit_mask).eq(0))
+  )
+
+  return image.updateMask(mask).divide(10000)
+
+
+# Load Sentinel-2 TOA reflectance data.
+dataset = (
+    ee.ImageCollection('COPERNICUS/S2')
+    .filterDate('2018-01-01', '2018-01-31')
+    # Pre-filter to get less cloudy granules.
+    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
+    .map(mask_s2_clouds)
+)
+
+rgb_vis = {'min': 0.0, 'max': 0.3, 'bands': ['B4', 'B3', 'B2']}
+
+# Create and display the map.
+m = geemap.Map()
+m.set_center(-9.1695, 38.6917, 12)
+m.add_layer(dataset.median(), rgb_vis, 'RGB')
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.py b/examples/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.py
new file mode 100644
index 000000000..848056f08
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.py
@@ -0,0 +1,58 @@
+s2_sr = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
+s2_clouds = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
+
+start_date = ee.Date('2019-01-01')
+end_date = ee.Date('2019-03-01')
+max_cloud_probability = 65
+region = ee.Geometry.Rectangle(
+    coords=[-76.5, 2.0, -74, 4.0], geodesic=False, proj='EPSG:4326'
+)
+
+
+def maskClouds(img):
+  clouds = ee.Image(img.get('cloud_mask')).select('probability')
+  is_not_cloud = clouds.lt(max_cloud_probability)
+  return img.updateMask(is_not_cloud)
+
+
+# The masks for the 10m bands sometimes do not exclude bad data at
+# scene edges, so we apply masks from the 20m and 60m bands as well.
+# Example asset that needs this operation:
+# COPERNICUS/S2_CLOUD_PROBABILITY/20190301T000239_20190301T000238_T55GDP
+def maskEdges(s2_img):
+  return s2_img.updateMask(
+      s2_img.select('B8A').mask().updateMask(s2_img.select('B9').mask())
+  )
+
+
+# Filter input collections by desired data range and region.
+criteria = ee.Filter.And(
+    ee.Filter.bounds(region), ee.Filter.date(start_date, end_date)
+)
+s2_sr = s2_sr.filter(criteria).map(maskEdges)
+s2_clouds = s2_clouds.filter(criteria)
+
+# Join S2 SR with cloud probability dataset to add cloud mask.
+s2_sr_with_cloud_mask = ee.Join.saveFirst('cloud_mask').apply(
+    primary=s2_sr,
+    secondary=s2_clouds,
+    condition=ee.Filter.equals(
+        leftField='system:index', rightField='system:index'
+    ),
+)
+
+s_2_cloud_masked = (
+    ee.ImageCollection(s2_sr_with_cloud_mask).map(maskClouds).median()
+)
+
+rgb_vis = {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']}
+
+m = geemap.Map()
+m.set_center(-75, 3, 12)
+m.add_layer(
+    s_2_cloud_masked,
+    rgb_vis,
+    'S2 SR masked at ' + str(max_cloud_probability) + '%',
+    True,
+)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S3_OLCI.py b/examples/COPERNICUS/COPERNICUS_S3_OLCI.py
new file mode 100644
index 000000000..bdb6c015e
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S3_OLCI.py
@@ -0,0 +1,17 @@
+dataset = ee.ImageCollection('COPERNICUS/S3/OLCI').filterDate(
+    '2018-04-01', '2018-04-04'
+)
+
+# Select bands for visualization and apply band-specific scale factors.
+rgb = (
+    dataset.select(['Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance'])
+    .median()
+    .multiply(ee.Image([0.00876539, 0.0123538, 0.0115198]))
+)  # Convert to radiance units.
+
+vis_params = {'min': 0, 'max': 6, 'gamma': 1.5}
+
+m = geemap.Map()
+m.set_center(46.043, 1.45, 5)
+m.add_layer(rgb, vis_params, 'RGB')
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.py b/examples/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.py
new file mode 100644
index 000000000..891a0e3a2
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.py
@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')
+    .select('NO2_column_number_density')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.0002,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P N02')
+m.set_center(65.27, 24.11, 4)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.py b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.py
new file mode 100644
index 000000000..b893ccc31
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.py
@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')
+    .select('absorbing_aerosol_index')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': -1,
+    'max': 2.0,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P Aerosol')
+m.set_center(-118.82, 36.1, 5)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.py b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.py
new file mode 100644
index 000000000..fb2a422a0
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.py
@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')
+    .select('CH4_column_volume_mixing_ratio_dry_air')
+    .filterDate('2019-06-01', '2019-07-16')
+)
+
+band_viz = {
+    'min': 1750,
+    'max': 1900,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P CH4')
+m.set_center(0.0, 0.0, 2)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.py b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.py
new file mode 100644
index 000000000..bfccb876b
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.py
@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')
+    .select('CO_column_number_density')
+    .filterDate('2019-06-01', '2019-06-11')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.05,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P CO')
+m.set_center(-25.01, -4.28, 4)
+m
diff --git a/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.py b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.py
new file mode 100644
index 000000000..bda1a3e51
--- /dev/null
+++ b/examples/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.py
@@ -0,0 +1,16 @@
+collection = (
+    ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')
+    .select('tropospheric_NO2_column_number_density')
+    .filterDate('2019-06-01', '2019-06-06')
+)
+
+band_viz = {
+    'min': 0,
+    'max': 0.0002,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(collection.mean(), band_viz, 'S5P N02')
+m.set_center(65.27, 24.11, 4)
+m
diff --git a/examples/ECMWF/ECMWF_ERA5_LAND_HOURLY.py b/examples/ECMWF/ECMWF_ERA5_LAND_HOURLY.py
new file mode 100644
index 000000000..f523342d2
--- /dev/null
+++ b/examples/ECMWF/ECMWF_ERA5_LAND_HOURLY.py
@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY').filter(
+    ee.Filter.date('2020-07-01', '2020-07-02')
+)
+
+visualization = {
+    'bands': ['temperature_2m'],
+    'min': 250.0,
+    'max': 320.0,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(22.2, 21.2, 0)
+m.add_layer(dataset, visualization, 'Air temperature [K] at 2m height')
+m
diff --git a/examples/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.py b/examples/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.py
new file mode 100644
index 000000000..68a26812f
--- /dev/null
+++ b/examples/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.py
@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR').first()
+
+visualization = {
+    'bands': ['temperature_2m'],
+    'min': 250,
+    'max': 320,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(70, 45, 3)
+m.add_layer(
+    dataset, visualization, 'Air temperature [K] at 2m height', True, 0.8
+)
+m
diff --git a/examples/ECMWF/ECMWF_ERA5_MONTHLY.py b/examples/ECMWF/ECMWF_ERA5_MONTHLY.py
new file mode 100644
index 000000000..d3d48af5c
--- /dev/null
+++ b/examples/ECMWF/ECMWF_ERA5_MONTHLY.py
@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('ECMWF/ERA5/MONTHLY')
+
+visualization = {
+    'bands': ['mean_2m_air_temperature'],
+    'min': 250.0,
+    'max': 320.0,
+    'palette': [
+        '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
+        '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
+        'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(22.2, 21.2, 0)
+m.add_layer(
+    dataset, visualization, 'Monthly average air temperature [K] at 2m height'
+)
+m
diff --git a/examples/EO1/EO1_HYPERION.py b/examples/EO1/EO1_HYPERION.py
new file mode 100644
index 000000000..21bf28383
--- /dev/null
+++ b/examples/EO1/EO1_HYPERION.py
@@ -0,0 +1,11 @@
+dataset = ee.ImageCollection('EO1/HYPERION').filter(
+    ee.Filter.date('2016-01-01', '2017-03-01')
+)
+
+rgb = dataset.select(['B050', 'B023', 'B015'])
+rgb_vis = {'min': 1000.0, 'max': 14000.0, 'gamma': 2.5}
+
+m = geemap.Map()
+m.set_center(162.0044, -77.3463, 9)
+m.add_layer(rgb.median(), rgb_vis, 'RGB')
+m
diff --git a/examples/ESA/ESA_CCI_FireCCI_5_1.py b/examples/ESA/ESA_CCI_FireCCI_5_1.py
new file mode 100644
index 000000000..9d0e34362
--- /dev/null
+++ b/examples/ESA/ESA_CCI_FireCCI_5_1.py
@@ -0,0 +1,24 @@
+# Visualize FireCCI51 for one year
+dataset = ee.ImageCollection('ESA/CCI/FireCCI/5_1').filterDate(
+    '2020-01-01', '2020-12-31'
+)
+
+burned_area = dataset.select('BurnDate')
+max_ba = burned_area.max()
+
+# Use a circular palette to assign colors to date of first detection
+ba_vis = {
+    'min': 1,
+    'max': 366,
+    'palette': [
+        'ff0000', 'fd4100', 'fb8200', 'f9c400', 'f2ff00', 'b6ff05',
+        '7aff0a', '3eff0f', '02ff15', '00ff55', '00ff99', '00ffdd',
+        '00ddff', '0098ff', '0052ff', '0210ff', '3a0dfb', '7209f6',
+        'a905f1', 'e102ed', 'ff00cc', 'ff0089', 'ff0047', 'ff0004'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(0, 18, 2.1)
+m.add_layer(max_ba, ba_vis, 'Burned Area')
+m
diff --git a/examples/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3.py b/examples/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3.py
new file mode 100644
index 000000000..d8ae48d74
--- /dev/null
+++ b/examples/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3.py
@@ -0,0 +1,7 @@
+dataset = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3')
+landcover = dataset.select('landcover')
+
+m = geemap.Map()
+m.set_center(-88.6, 26.4, 3)
+m.add_layer(landcover, {}, 'Landcover')
+m
diff --git a/examples/ESA/ESA_WorldCover_v200.py b/examples/ESA/ESA_WorldCover_v200.py
new file mode 100644
index 000000000..31c05b453
--- /dev/null
+++ b/examples/ESA/ESA_WorldCover_v200.py
@@ -0,0 +1,8 @@
+dataset = ee.ImageCollection('ESA/WorldCover/v200').first()
+
+visualization = {'bands': ['Map']}
+
+m = geemap.Map()
+m.center_object(dataset)
+m.add_layer(dataset, visualization, 'Landcover')
+m
diff --git a/examples/FAO/FAO_GAUL_2015_level1.py b/examples/FAO/FAO_GAUL_2015_level1.py
new file mode 100644
index 000000000..951f5b40c
--- /dev/null
+++ b/examples/FAO/FAO_GAUL_2015_level1.py
@@ -0,0 +1,8 @@
+dataset = ee.FeatureCollection('FAO/GAUL/2015/level1')
+
+dataset = dataset.style(fillColor='b5ffb4', color='00909F', width=1.0)
+
+m = geemap.Map()
+m.set_center(7.82, 49.1, 4)
+m.add_layer(dataset, {}, 'First Level Administrative Units')
+m
diff --git a/examples/FIRMS/FIRMS.py b/examples/FIRMS/FIRMS.py
new file mode 100644
index 000000000..919b1c3ca
--- /dev/null
+++ b/examples/FIRMS/FIRMS.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('FIRMS').filter(
+    ee.Filter.date('2018-08-01', '2018-08-10')
+)
+
+fires = dataset.select('T21')
+
+fires_vis = {'min': 325.0, 'max': 400.0, 'palette': ['red', 'orange', 'yellow']}
+
+m = geemap.Map()
+m.set_center(-119.086, 47.295, 6)
+m.add_layer(fires, fires_vis, 'Fires')
+m
diff --git a/examples/GOOGLE/GOOGLE_Research_open-buildings_v2_polygons.py b/examples/GOOGLE/GOOGLE_Research_open-buildings_v2_polygons.py
new file mode 100644
index 000000000..35a189919
--- /dev/null
+++ b/examples/GOOGLE/GOOGLE_Research_open-buildings_v2_polygons.py
@@ -0,0 +1,13 @@
+# Visualization of GOOGLE/Research/open-buildings/v2/polygons.
+t = ee.FeatureCollection('GOOGLE/Research/open-buildings/v2/polygons')
+
+t_060_065 = t.filter('confidence >= 0.60 && confidence < 0.65')
+t_065_070 = t.filter('confidence >= 0.65 && confidence < 0.70')
+t_gte_070 = t.filter('confidence >= 0.70');
+
+m = geemap.Map()
+m.set_center(3.389, 6.492, 17)
+m.add_layer(t_060_065, {'color': 'FF0000'}, 'Buildings confidence [0.60; 0.65)')
+m.add_layer(t_065_070, {'color': 'FFFF00'}, 'Buildings confidence [0.65; 0.70)')
+m.add_layer(t_gte_070, {'color': '00FF00'}, 'Buildings confidence >= 0.70')
+m
diff --git a/examples/GOOGLE/GOOGLE_Research_open-buildings_v3_polygons.py b/examples/GOOGLE/GOOGLE_Research_open-buildings_v3_polygons.py
new file mode 100644
index 000000000..66ee00ad0
--- /dev/null
+++ b/examples/GOOGLE/GOOGLE_Research_open-buildings_v3_polygons.py
@@ -0,0 +1,13 @@
+# Visualization of GOOGLE/Research/open-buildings/v3/polygons.
+t = ee.FeatureCollection('GOOGLE/Research/open-buildings/v3/polygons')
+
+t_065_070 = t.filter('confidence >= 0.65 && confidence < 0.7')
+t_070_075 = t.filter('confidence >= 0.7 && confidence < 0.75')
+t_gte_075 = t.filter('confidence >= 0.75');
+
+m = geemap.Map()
+m.set_center(3.389, 6.492, 17)
+m.add_layer(t_065_070, {'color': 'FF0000'}, 'Buildings confidence [0.65; 0.7)')
+m.add_layer(t_070_075, {'color': 'FFFF00'}, 'Buildings confidence [0.7; 0.75)')
+m.add_layer(t_gte_075, {'color': '00FF00'}, 'Buildings confidence >= 0.75')
+m
diff --git a/examples/GRIDMET/GRIDMET_DROUGHT.py b/examples/GRIDMET/GRIDMET_DROUGHT.py
new file mode 100644
index 000000000..f0a4fa905
--- /dev/null
+++ b/examples/GRIDMET/GRIDMET_DROUGHT.py
@@ -0,0 +1,63 @@
+collection = ee.ImageCollection('GRIDMET/DROUGHT')
+
+# Filter by date
+d_s = '2020-03-30'
+d_e = '2020-03-30'
+d_s_utc = ee.Date(d_s, 'GMT')
+d_e_utc = ee.Date(d_e, 'GMT')
+filtered = collection.filterDate(d_s_utc, d_e_utc.advance(1, 'day'))
+
+# Select variables pdsi and z
+pdsi = filtered.select('pdsi')
+z = filtered.select('z')
+
+# Select variables for SPI/SPEI/EDDI
+# Note that possible timescales for SPI/SPEI/EDDI are:
+# 14d (14 day), 30d (30 day), 90d (90 day), 180d (180 day),
+# 1y (1 year), 2y (2 year), 5y (5 year)
+# Here we choose 2years = 48 months
+spi_2_y = filtered.select('spi2y')
+spei_2_y = filtered.select('spei2y')
+eddi_2_y = filtered.select('spei2y')
+
+# Make a color palette that is similar to USDM drought classification
+usdm_colors = [
+    '#0000aa',
+    '#0000ff',
+    '#00aaff',
+    '#00ffff',
+    '#aaff55',
+    '#ffffff',
+    '#ffff00',
+    '#fcd37f',
+    '#ffaa00',
+    '#e60000',
+    '#730000',
+]
+
+# Make color options for standardized variables spi/spei/eddi
+min_colorbar = -2.5
+max_colorbar = 2.5
+colorbar_options_1 = {
+    'min': min_colorbar,
+    'max': max_colorbar,
+    'palette': usdm_colors,
+}
+
+# Make color options for Palmer variables psdi/z
+min_colorbar = -6
+max_colorbar = 6
+colorbar_options_2 = {
+    'min': min_colorbar,
+    'max': max_colorbar,
+    'palette': usdm_colors,
+}
+
+# Add map layers to Google Map
+m = geemap.Map()
+m.add_layer(ee.Image(pdsi.first()), colorbar_options_2, 'pdsi')
+m.add_layer(ee.Image(z.first()), colorbar_options_2, 'Palmer-z')
+m.add_layer(ee.Image(spi_2_y.first()), colorbar_options_1, 'SPI-48months')
+m.add_layer(ee.Image(spei_2_y.first()), colorbar_options_1, 'SPEI-48months')
+m.add_layer(ee.Image(eddi_2_y.first()), colorbar_options_1, 'EDDI-48months')
+m
diff --git a/examples/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.py b/examples/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.py
new file mode 100644
index 000000000..7b18b9919
--- /dev/null
+++ b/examples/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET').filter(
+    ee.Filter.date('2018-08-01', '2018-08-15')
+)
+
+maximum_temperature = dataset.select('tmmx')
+
+maximum_temperature_vis = {
+    'min': 290.0,
+    'max': 314.0,
+    'palette': ['d8d8d8', '4addff', '5affa3', 'f2ff89', 'ff725c'],
+}
+
+m = geemap.Map()
+m.set_center(-115.356, 38.686, 5)
+m.add_layer(maximum_temperature, maximum_temperature_vis, 'Maximum Temperature')
+m
diff --git a/examples/JAXA/JAXA_ALOS_AW3D30_V3_2.py b/examples/JAXA/JAXA_ALOS_AW3D30_V3_2.py
new file mode 100644
index 000000000..b09184d44
--- /dev/null
+++ b/examples/JAXA/JAXA_ALOS_AW3D30_V3_2.py
@@ -0,0 +1,21 @@
+dataset = ee.ImageCollection('JAXA/ALOS/AW3D30/V3_2')
+elevation = dataset.select('DSM')
+
+# Use a projection from one of the image tiles,
+# rather than using the default projection returned by .mosaic().
+proj = elevation.first().select(0).projection()
+slope_reprojected = ee.Terrain.slope(
+    elevation.mosaic().setDefaultProjection(proj)
+)
+
+elevation_vis = {
+    'min': 0,
+    'max': 5000,
+    'palette': ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff'],
+}
+
+m = geemap.Map()
+m.set_center(138.73, 35.36, 11)
+m.add_layer(elevation, elevation_vis, 'Elevation')
+m.add_layer(slope_reprojected, {'min': 0, 'max': 45}, 'Slope')
+m
diff --git a/examples/JAXA/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR.py b/examples/JAXA/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR.py
new file mode 100644
index 000000000..5159cf787
--- /dev/null
+++ b/examples/JAXA/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR.py
@@ -0,0 +1,10 @@
+collection = ee.ImageCollection(
+    'JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR'
+).filterBounds(ee.Geometry.Point(143, -5))
+
+image = collection.first()
+
+m = geemap.Map()
+m.add_layer(image.select(['HH']), {'min': 0, 'max': 8000}, 'HH polarization')
+m.center_object(image)
+m
diff --git a/examples/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR.py b/examples/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR.py
new file mode 100644
index 000000000..1a6afcbc5
--- /dev/null
+++ b/examples/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR.py
@@ -0,0 +1,11 @@
+dataset = ee.ImageCollection('JAXA/ALOS/PALSAR/YEARLY/SAR').filter(
+    ee.Filter.date('2017-01-01', '2018-01-01')
+)
+
+sar_hh = dataset.select('HH')
+sar_hh_vis = {'min': 0.0, 'max': 10000.0}
+
+m = geemap.Map()
+m.set_center(136.85, 37.37, 4)
+m.add_layer(sar_hh, sar_hh_vis, 'SAR HH')
+m
diff --git a/examples/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.py b/examples/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.py
new file mode 100644
index 000000000..932987da3
--- /dev/null
+++ b/examples/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.py
@@ -0,0 +1,22 @@
+dataset = ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational').filter(
+    ee.Filter.date('2018-08-06', '2018-08-07')
+)
+precipitation = dataset.select('hourlyPrecipRate')
+precipitation_vis = {
+    'min': 0.0,
+    'max': 30.0,
+    'palette': [
+        '1621a2',
+        'ffffff',
+        '03ffff',
+        '13ff03',
+        'efff00',
+        'ffb103',
+        'ff2300',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(-90.7, 26.12, 2)
+m.add_layer(precipitation, precipitation_vis, 'Precipitation')
+m
diff --git a/examples/JRC/JRC_GSW1_4_GlobalSurfaceWater.py b/examples/JRC/JRC_GSW1_4_GlobalSurfaceWater.py
new file mode 100644
index 000000000..08005ebb8
--- /dev/null
+++ b/examples/JRC/JRC_GSW1_4_GlobalSurfaceWater.py
@@ -0,0 +1,13 @@
+dataset = ee.Image('JRC/GSW1_4/GlobalSurfaceWater')
+
+visualization = {
+    'bands': ['occurrence'],
+    'min': 0.0,
+    'max': 100.0,
+    'palette': ['ffffff', 'ffbbbb', '0000ff'],
+}
+
+m = geemap.Map()
+m.set_center(59.414, 45.182, 6)
+m.add_layer(dataset, visualization, 'Occurrence')
+m
diff --git a/examples/LANDSAT/LANDSAT_LC08_C02_T1.py b/examples/LANDSAT/LANDSAT_LC08_C02_T1.py
new file mode 100644
index 000000000..f0615ba77
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LC08_C02_T1.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1').filterDate(
+    '2017-01-01', '2017-12-31'
+)
+
+true_color_432 = dataset.select(['B4', 'B3', 'B2'])
+
+true_color_432_vis = {'min': 0.0, 'max': 30000.0}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_432, true_color_432_vis, 'True Color (432)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LC08_C02_T2_L2.py b/examples/LANDSAT/LANDSAT_LC08_C02_T2_L2.py
new file mode 100644
index 000000000..f96964a04
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LC08_C02_T2_L2.py
@@ -0,0 +1,22 @@
+dataset = ee.ImageCollection('LANDSAT/LC08/C02/T2_L2').filterDate(
+    '2021-05-01', '2021-06-01'
+)
+
+
+# Applies scaling factors.
+def apply_scale_factors(image):
+  optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
+  thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0)
+  return image.addBands(optical_bands, None, True).addBands(
+      thermal_bands, None, True
+  )
+
+
+dataset = dataset.map(apply_scale_factors)
+
+visualization = {'bands': ['SR_B4', 'SR_B3', 'SR_B2'], 'min': 0.0, 'max': 0.3}
+
+m = geemap.Map()
+m.set_center(-83, 24, 8)
+m.add_layer(dataset, visualization, 'True Color (432)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LC09_C02_T1.py b/examples/LANDSAT/LANDSAT_LC09_C02_T1.py
new file mode 100644
index 000000000..d4b7649cf
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LC09_C02_T1.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LC09/C02/T1').filterDate(
+    '2022-01-01', '2022-02-01'
+)
+
+true_color_432 = dataset.select(['B4', 'B3', 'B2'])
+
+true_color_432_vis = {'min': 0.0, 'max': 30000.0}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_432, true_color_432_vis, 'True Color (432)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LC09_C02_T1_TOA.py b/examples/LANDSAT/LANDSAT_LC09_C02_T1_TOA.py
new file mode 100644
index 000000000..360840cd0
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LC09_C02_T1_TOA.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA').filterDate(
+    '2022-01-01', '2022-02-01'
+)
+
+true_color_432 = dataset.select(['B4', 'B3', 'B2'])
+
+true_color_432_vis = {'min': 0.0, 'max': 0.4}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_432, true_color_432_vis, 'True Color (432)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LE07_C02_T1.py b/examples/LANDSAT/LANDSAT_LE07_C02_T1.py
new file mode 100644
index 000000000..20a544716
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LE07_C02_T1.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LE07/C02/T1').filterDate(
+    '1999-01-01', '2002-12-31'
+)
+
+true_color_321 = dataset.select(['B3', 'B2', 'B1'])
+
+true_color_321_vis = {}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_321, true_color_321_vis, 'True Color (321)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LE07_C02_T1_TOA.py b/examples/LANDSAT/LANDSAT_LE07_C02_T1_TOA.py
new file mode 100644
index 000000000..e358448d5
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LE07_C02_T1_TOA.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LE07/C02/T1_TOA').filterDate(
+    '1999-01-01', '2002-12-31'
+)
+
+true_color_321 = dataset.select(['B3', 'B2', 'B1'])
+
+true_color_321_vis = {'min': 0.0, 'max': 0.4, 'gamma': 1.2}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_321, true_color_321_vis, 'True Color (321)')
+m
diff --git a/examples/LANDSAT/LANDSAT_LT05_C02_T1_TOA.py b/examples/LANDSAT/LANDSAT_LT05_C02_T1_TOA.py
new file mode 100644
index 000000000..6759e2ce8
--- /dev/null
+++ b/examples/LANDSAT/LANDSAT_LT05_C02_T1_TOA.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('LANDSAT/LT05/C02/T1_TOA').filterDate(
+    '2011-01-01', '2011-12-31'
+)
+
+true_color_321 = dataset.select(['B3', 'B2', 'B1'])
+
+true_color_321_vis = {'min': 0.0, 'max': 0.4, 'gamma': 1.2}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color_321, true_color_321_vis, 'True Color (321)')
+m
diff --git a/examples/LARSE/LARSE_GEDI_GEDI02_A_002_MONTHLY.py b/examples/LARSE/LARSE_GEDI_GEDI02_A_002_MONTHLY.py
new file mode 100644
index 000000000..e0767d89e
--- /dev/null
+++ b/examples/LARSE/LARSE_GEDI_GEDI02_A_002_MONTHLY.py
@@ -0,0 +1,22 @@
+def quality_mask(im):
+  return im.updateMask(im.select('quality_flag').eq(1)).updateMask(
+      im.select('degrade_flag').eq(0)
+  )
+
+
+dataset = (
+    ee.ImageCollection('LARSE/GEDI/GEDI02_A_002_MONTHLY')
+    .map(quality_mask)
+    .select('rh98')
+)
+
+gedi_vis = {
+    'min': 1,
+    'max': 60,
+    'palette': 'darkred,red,orange,green,darkgreen',
+}
+
+m = geemap.Map()
+m.set_center(-74.803466, -9.342209, 10)
+m.add_layer(dataset, gedi_vis, 'rh98')
+m
diff --git a/examples/MODIS/MODIS_006_MCD12Q1.py b/examples/MODIS/MODIS_006_MCD12Q1.py
new file mode 100644
index 000000000..13e2515ea
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MCD12Q1.py
@@ -0,0 +1,18 @@
+dataset = ee.ImageCollection('MODIS/006/MCD12Q1')
+
+igbp_land_cover = dataset.select('LC_Type1')
+
+igbp_land_cover_vis = {
+    'min': 1.0,
+    'max': 17.0,
+    'palette': [
+        '05450a', '086a10', '54a708', '78d203', '009900', 'c6b044', 'dcd159',
+        'dade48', 'fbff13', 'b6ff05', '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c',
+        '69fff8', 'f9ffa4', '1c0dff'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(igbp_land_cover, igbp_land_cover_vis, 'IGBP Land Cover')
+m
diff --git a/examples/MODIS/MODIS_006_MCD43A4.py b/examples/MODIS/MODIS_006_MCD43A4.py
new file mode 100644
index 000000000..51acb0ccb
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MCD43A4.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('MODIS/006/MCD43A4').filter(
+    ee.Filter.date('2018-04-01', '2018-06-01')
+)
+
+true_color = dataset.select([
+    'Nadir_Reflectance_Band1',
+    'Nadir_Reflectance_Band4',
+    'Nadir_Reflectance_Band3',
+])
+
+true_color_vis = {'min': 0, 'max': 4000, 'gamma': 1.4}
+
+m = geemap.Map()
+m.set_center(-7.03, 31.06, 2)
+m.add_layer(true_color, true_color_vis, 'True Color')
+m
diff --git a/examples/MODIS/MODIS_006_MCD64A1.py b/examples/MODIS/MODIS_006_MCD64A1.py
new file mode 100644
index 000000000..509ce7181
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MCD64A1.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('MODIS/006/MCD64A1').filter(
+    ee.Filter.date('2017-01-01', '2018-05-01')
+)
+
+burned_area = dataset.select('BurnDate')
+
+burned_area_vis = {
+    'min': 30,
+    'max': 341,
+    'palette': ['4e0400', '951003', 'c61503', 'ff1901'],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(burned_area, burned_area_vis, 'Burned Area')
+m
diff --git a/examples/MODIS/MODIS_006_MOD09GQ.py b/examples/MODIS/MODIS_006_MOD09GQ.py
new file mode 100644
index 000000000..e4d369081
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MOD09GQ.py
@@ -0,0 +1,14 @@
+dataset = ee.ImageCollection('MODIS/006/MOD09GQ').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+false_color_vis = {
+    'min': -100,
+    'max': 8000,
+    'bands': ['sur_refl_b02', 'sur_refl_b02', 'sur_refl_b01'],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(dataset, false_color_vis, 'False Color')
+m
diff --git a/examples/MODIS/MODIS_006_MOD11A1.py b/examples/MODIS/MODIS_006_MOD11A1.py
new file mode 100644
index 000000000..1415b96c6
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MOD11A1.py
@@ -0,0 +1,26 @@
+dataset = ee.ImageCollection('MODIS/006/MOD11A1').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+land_surface_temperature = dataset.select('LST_Day_1km')
+
+land_surface_temperature_vis = {
+    'min': 13000,
+    'max': 16500,
+    'palette': [
+        '040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
+        '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
+        '3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
+        'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
+        'ff0000', 'de0101', 'c21301', 'a71001', '911003'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(
+    land_surface_temperature,
+    land_surface_temperature_vis,
+    'Land Surface Temperature',
+)
+m
diff --git a/examples/MODIS/MODIS_006_MOD13Q1.py b/examples/MODIS/MODIS_006_MOD13Q1.py
new file mode 100644
index 000000000..ca3da60b5
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MOD13Q1.py
@@ -0,0 +1,20 @@
+dataset = ee.ImageCollection('MODIS/006/MOD13Q1').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+ndvi = dataset.select('NDVI')
+
+ndvi_vis = {
+    'min': 0,
+    'max': 8000,
+    'palette': [
+        'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
+        '66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
+        '012e01', '011d01', '011301'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(ndvi, ndvi_vis, 'NDVI')
+m
diff --git a/examples/MODIS/MODIS_006_MOD16A2.py b/examples/MODIS/MODIS_006_MOD16A2.py
new file mode 100644
index 000000000..ad5992779
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MOD16A2.py
@@ -0,0 +1,26 @@
+dataset = ee.ImageCollection('MODIS/006/MOD16A2').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+evapotranspiration = dataset.select('ET')
+
+evapotranspiration_vis = {
+    'min': 0,
+    'max': 300,
+    'palette': [
+        'ffffff',
+        'fcd163',
+        '99b718',
+        '66a000',
+        '3e8601',
+        '207401',
+        '056201',
+        '004c00',
+        '011301',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(evapotranspiration, evapotranspiration_vis, 'Evapotranspiration')
+m
diff --git a/examples/MODIS/MODIS_006_MOD17A2H.py b/examples/MODIS/MODIS_006_MOD17A2H.py
new file mode 100644
index 000000000..9cd467e7d
--- /dev/null
+++ b/examples/MODIS/MODIS_006_MOD17A2H.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('MODIS/006/MOD17A2H').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+gpp = dataset.select('Gpp')
+
+gpp_vis = {'min': 0, 'max': 600, 'palette': ['bbe029', '0a9501', '074b03']}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(gpp, gpp_vis, 'GPP')
+m
diff --git a/examples/MODIS/MODIS_061_MCD12Q1.py b/examples/MODIS/MODIS_061_MCD12Q1.py
new file mode 100644
index 000000000..9c5cd181f
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MCD12Q1.py
@@ -0,0 +1,18 @@
+dataset = ee.ImageCollection('MODIS/061/MCD12Q1')
+
+igbp_land_cover = dataset.select('LC_Type1')
+
+igbp_land_cover_vis = {
+    'min': 1.0,
+    'max': 17.0,
+    'palette': [
+        '05450a', '086a10', '54a708', '78d203', '009900', 'c6b044', 'dcd159',
+        'dade48', 'fbff13', 'b6ff05', '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c',
+        '69fff8', 'f9ffa4', '1c0dff'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(igbp_land_cover, igbp_land_cover_vis, 'IGBP Land Cover')
+m
diff --git a/examples/MODIS/MODIS_061_MCD15A3H.py b/examples/MODIS/MODIS_061_MCD15A3H.py
new file mode 100644
index 000000000..a5a7bcebe
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MCD15A3H.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('MODIS/061/MCD15A3H')
+
+default_visualization = dataset.first().select('Fpar')
+
+default_visualization_vis = {
+    'min': 0.0,
+    'max': 100.0,
+    'palette': ['e1e4b4', '999d60', '2ec409', '0a4b06'],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(
+    default_visualization, default_visualization_vis, 'Default visualization'
+)
+m
diff --git a/examples/MODIS/MODIS_061_MCD43A4.py b/examples/MODIS/MODIS_061_MCD43A4.py
new file mode 100644
index 000000000..9bc8839cc
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MCD43A4.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('MODIS/061/MCD43A4').filter(
+    ee.Filter.date('2018-04-01', '2018-06-01')
+)
+
+true_color = dataset.select([
+    'Nadir_Reflectance_Band1',
+    'Nadir_Reflectance_Band4',
+    'Nadir_Reflectance_Band3',
+])
+
+true_color_vis = {'min': 0, 'max': 4000, 'gamma': 1.4}
+
+m = geemap.Map()
+m.set_center(-7.03, 31.06, 2)
+m.add_layer(true_color, true_color_vis, 'True Color')
+m
\ No newline at end of file
diff --git a/examples/MODIS/MODIS_061_MCD64A1.py b/examples/MODIS/MODIS_061_MCD64A1.py
new file mode 100644
index 000000000..0cc893d69
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MCD64A1.py
@@ -0,0 +1,13 @@
+dataset = ee.ImageCollection('MODIS/061/MCD64A1').filter(
+    ee.Filter.date('2017-01-01', '2018-05-01')
+)
+burned_area = dataset.select('BurnDate')
+burned_area_vis = {
+    'min': 30,
+    'max': 341,
+    'palette': ['4e0400', '951003', 'c61503', 'ff1901'],
+}
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(burned_area, burned_area_vis, 'Burned Area')
+m
diff --git a/examples/MODIS/MODIS_061_MOD09A1.py b/examples/MODIS/MODIS_061_MOD09A1.py
new file mode 100644
index 000000000..37e4c0604
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD09A1.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('MODIS/061/MOD09A1').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+true_color = dataset.select(['sur_refl_b01', 'sur_refl_b04', 'sur_refl_b03'])
+
+true_color_vis = {'min': -100.0, 'max': 3000.0}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 6)
+m.add_layer(true_color, true_color_vis, 'True Color')
+m
diff --git a/examples/MODIS/MODIS_061_MOD09GQ.py b/examples/MODIS/MODIS_061_MOD09GQ.py
new file mode 100644
index 000000000..e1a7ec7ed
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD09GQ.py
@@ -0,0 +1,14 @@
+dataset = ee.ImageCollection('MODIS/061/MOD09GQ').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+false_color_vis = {
+    'min': -100,
+    'max': 8000,
+    'bands': ['sur_refl_b02', 'sur_refl_b02', 'sur_refl_b01'],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(dataset, false_color_vis, 'False Color')
+m
diff --git a/examples/MODIS/MODIS_061_MOD11A1.py b/examples/MODIS/MODIS_061_MOD11A1.py
new file mode 100644
index 000000000..3f6ba549e
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD11A1.py
@@ -0,0 +1,26 @@
+dataset = ee.ImageCollection('MODIS/061/MOD11A1').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+land_surface_temperature = dataset.select('LST_Day_1km')
+
+land_surface_temperature_vis = {
+    'min': 13000.0,
+    'max': 16500.0,
+    'palette': [
+        '040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
+        '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
+        '3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
+        'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
+        'ff0000', 'de0101', 'c21301', 'a71001', '911003'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(
+    land_surface_temperature,
+    land_surface_temperature_vis,
+    'Land Surface Temperature',
+)
+m
diff --git a/examples/MODIS/MODIS_061_MOD11A2.py b/examples/MODIS/MODIS_061_MOD11A2.py
new file mode 100644
index 000000000..3d03103de
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD11A2.py
@@ -0,0 +1,26 @@
+dataset = ee.ImageCollection('MODIS/061/MOD11A2').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+land_surface_temperature = dataset.select('LST_Day_1km')
+
+land_surface_temperature_vis = {
+    'min': 14000.0,
+    'max': 16000.0,
+    'palette': [
+        '040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
+        '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
+        '3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
+        'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
+        'ff0000', 'de0101', 'c21301', 'a71001', '911003'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(
+    land_surface_temperature,
+    land_surface_temperature_vis,
+    'Land Surface Temperature',
+)
+m
diff --git a/examples/MODIS/MODIS_061_MOD13A2.py b/examples/MODIS/MODIS_061_MOD13A2.py
new file mode 100644
index 000000000..09e9aa987
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD13A2.py
@@ -0,0 +1,20 @@
+dataset = ee.ImageCollection('MODIS/061/MOD13A2').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+ndvi = dataset.select('NDVI')
+
+ndvi_vis = {
+    'min': 0,
+    'max': 9000,
+    'palette': [
+        'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
+        '66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
+        '012e01', '011d01', '011301'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(ndvi, ndvi_vis, 'NDVI')
+m
diff --git a/examples/MODIS/MODIS_061_MOD13Q1.py b/examples/MODIS/MODIS_061_MOD13Q1.py
new file mode 100644
index 000000000..f318bcc48
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD13Q1.py
@@ -0,0 +1,20 @@
+dataset = ee.ImageCollection('MODIS/061/MOD13Q1').filter(
+    ee.Filter.date('2018-01-01', '2018-05-01')
+)
+
+ndvi = dataset.select('NDVI')
+
+ndvi_vis = {
+    'min': 0,
+    'max': 8000,
+    'palette': [
+        'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
+        '66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
+        '012e01', '011d01', '011301'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(ndvi, ndvi_vis, 'NDVI')
+m
diff --git a/examples/MODIS/MODIS_061_MOD16A2.py b/examples/MODIS/MODIS_061_MOD16A2.py
new file mode 100644
index 000000000..d061546f6
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD16A2.py
@@ -0,0 +1,26 @@
+dataset = ee.ImageCollection('MODIS/061/MOD16A2').filter(
+    ee.Filter.date('2022-01-01', '2022-05-01')
+)
+
+evapotranspiration = dataset.select('ET')
+
+evapotranspiration_vis = {
+    'min': 0,
+    'max': 300,
+    'palette': [
+        'ffffff',
+        'fcd163',
+        '99b718',
+        '66a000',
+        '3e8601',
+        '207401',
+        '056201',
+        '004c00',
+        '011301',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(0, 0, 2)
+m.add_layer(evapotranspiration, evapotranspiration_vis, 'Evapotranspiration')
+m
diff --git a/examples/MODIS/MODIS_061_MOD17A2H.py b/examples/MODIS/MODIS_061_MOD17A2H.py
new file mode 100644
index 000000000..8098d5741
--- /dev/null
+++ b/examples/MODIS/MODIS_061_MOD17A2H.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('MODIS/061/MOD17A2H').filter(
+    ee.Filter.date('2021-01-01', '2021-05-01')
+)
+
+gpp = dataset.select('Gpp')
+
+gpp_vis = {'min': 0, 'max': 600, 'palette': ['bbe029', '0a9501', '074b03']}
+
+m = geemap.Map()
+m.set_center(6.746, 46.529, 2)
+m.add_layer(gpp, gpp_vis, 'GPP')
+m
diff --git a/examples/MODIS/MODIS_MOD09GA_006_NDVI.py b/examples/MODIS/MODIS_MOD09GA_006_NDVI.py
new file mode 100644
index 000000000..fbe8b75db
--- /dev/null
+++ b/examples/MODIS/MODIS_MOD09GA_006_NDVI.py
@@ -0,0 +1,20 @@
+dataset = ee.ImageCollection('MODIS/MOD09GA_006_NDVI').filter(
+    ee.Filter.date('2018-04-01', '2018-06-01')
+)
+
+colorized = dataset.select('NDVI')
+
+colorized_vis = {
+    'min': 0,
+    'max': 1,
+    'palette': [
+        'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
+        '66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
+        '012e01', '011d01', '011301'
+    ],
+}
+
+m = geemap.Map()
+m.set_center(-7.03125, 31.0529339857, 2)
+m.add_layer(colorized, colorized_vis, 'Colorized')
+m
diff --git a/examples/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.py b/examples/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.py
new file mode 100644
index 000000000..d96480815
--- /dev/null
+++ b/examples/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.py
@@ -0,0 +1,17 @@
+dataset = ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001').filter(
+    ee.Filter.date('2018-11-01', '2018-12-01')
+)
+
+layer = dataset.select('Evap_tavg')
+
+band_viz = {
+    'min': 0.0,
+    'max': 0.00005,
+    'opacity': 1.0,
+    'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.set_center(30.0, 30.0, 2)
+m.add_layer(layer, band_viz, 'Average Evapotranspiration')
+m
diff --git a/examples/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.py b/examples/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.py
new file mode 100644
index 000000000..4c3363da5
--- /dev/null
+++ b/examples/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.py
@@ -0,0 +1,20 @@
+dataset = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H').filter(
+    ee.Filter.date('2010-06-01', '2010-06-02')
+)
+
+average_surface_skin_temperature_k = dataset.select('AvgSurfT_inst')
+
+average_surface_skin_temperature_k_vis = {
+    'min': 250.0,
+    'max': 300.0,
+    'palette': ['1303ff', '42fff6', 'f3ff40', 'ff5d0f'],
+}
+
+m = geemap.Map()
+m.set_center(71.72, 52.48, 3.0)
+m.add_layer(
+    average_surface_skin_temperature_k,
+    average_surface_skin_temperature_k_vis,
+    'Average Surface Skin Temperature [K]',
+)
+m
diff --git a/examples/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06.py b/examples/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06.py
new file mode 100644
index 000000000..a12e594d5
--- /dev/null
+++ b/examples/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06.py
@@ -0,0 +1,19 @@
+dataset = ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V06').filterDate(
+    '2019-01-01', '2020-01-01'
+)
+
+# Select the max precipitation and mask out low precipitation values.
+precipitation = dataset.select('precipitation').max()
+mask = precipitation.gt(0.25)
+precipitation = precipitation.updateMask(mask)
+
+palette = [
+  '000096','0064ff', '00b4ff', '33db80', '9beb4a',
+  'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'
+]
+precipitation_vis = {'min': 0.0, 'max': 1.5, 'palette': palette}
+
+m = geemap.Map()
+m.add_layer(precipitation, precipitation_vis, 'Precipitation (mm/hr)')
+m.set_center(-76, 33, 3)
+m
diff --git a/examples/NASA/NASA_GPM_L3_IMERG_V06.py b/examples/NASA/NASA_GPM_L3_IMERG_V06.py
new file mode 100644
index 000000000..9d95623a3
--- /dev/null
+++ b/examples/NASA/NASA_GPM_L3_IMERG_V06.py
@@ -0,0 +1,21 @@
+# GPM V6 30 minute data around hurricane Dorian for a single day.
+range = ee.Date('2019-09-03').getRange('day')
+dataset = ee.ImageCollection('NASA/GPM_L3/IMERG_V06').filter(
+    ee.Filter.date(range)
+)
+
+# Select the max precipitation and mask out low precipitation values.
+precipitation = dataset.select('precipitationCal').max()
+mask = precipitation.gt(0.5)
+precipitation = precipitation.updateMask(mask)
+
+palette = [
+  '000096','0064ff', '00b4ff', '33db80', '9beb4a',
+  'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'
+]
+precipitation_vis = {'min': 0, 'max': 15, 'palette': palette}
+
+m = geemap.Map()
+m.add_layer(precipitation, precipitation_vis, 'Precipitation (mm/hr)')
+m.set_center(-76, 33, 3)
+m
diff --git a/examples/NASA/NASA_GPM_L3_IMERG_V07.py b/examples/NASA/NASA_GPM_L3_IMERG_V07.py
new file mode 100644
index 000000000..bcd221be7
--- /dev/null
+++ b/examples/NASA/NASA_GPM_L3_IMERG_V07.py
@@ -0,0 +1,21 @@
+# GPM V7 30 minute data for a single day.
+range = ee.Date('2019-09-03').getRange('day')
+dataset = ee.ImageCollection('NASA/GPM_L3/IMERG_V07').filter(
+    ee.Filter.date(range)
+)
+
+# Select the max precipitation and mask out low precipitation values.
+precipitation = dataset.select('precipitation').max()
+mask = precipitation.gt(0.5)
+precipitation = precipitation.updateMask(mask)
+
+palette = [
+  '000096','0064ff', '00b4ff', '33db80', '9beb4a',
+  'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'
+]
+precipitation_vis = {'min': 0, 'max': 15, 'palette': palette}
+
+m = geemap.Map()
+m.add_layer(precipitation, precipitation_vis, 'Precipitation (mm/hr)')
+m.set_center(-76, 33, 3)
+m
diff --git a/examples/NASA/NASA_NASADEM_HGT_001.py b/examples/NASA/NASA_NASADEM_HGT_001.py
new file mode 100644
index 000000000..aa3f05cd0
--- /dev/null
+++ b/examples/NASA/NASA_NASADEM_HGT_001.py
@@ -0,0 +1,17 @@
+# Import the dataset and select the elevation band.
+dataset = ee.Image('NASA/NASADEM_HGT/001')
+
+elevation = dataset.select('elevation')
+
+# Add a white background image to the map.
+background = ee.Image(1)
+m = geemap.Map()
+m.set_center(17.93, 7.71, 2)
+m.add_layer(background, {'min': 0, 'max': 1})
+
+# Set elevation visualization properties.
+elevation_vis = {'min': 0, 'max': 2000}
+
+# Set elevation <= 0 as transparent and add to the map.
+m.add_layer(elevation.updateMask(elevation.gt(0)), elevation_vis, 'Elevation')
+m
diff --git a/examples/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.py b/examples/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.py
new file mode 100644
index 000000000..ea8c0ca1f
--- /dev/null
+++ b/examples/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI').filterDate(
+    '2016-01-01', '2016-01-31'
+)
+
+remote_sensing_reflectance = dataset.select(['Rrs_645', 'Rrs_555', 'Rrs_443'])
+
+remote_sensing_reflectance_vis = {'min': 0.0, 'max': 0.011}
+
+m = geemap.Map()
+m.set_center(-52.12, -46.13, 4)
+m.add_layer(
+    remote_sensing_reflectance,
+    remote_sensing_reflectance_vis,
+    'Remote Sensing Reflectance',
+)
+m
diff --git a/examples/NASA/NASA_SMAP_SPL4SMGP_007.py b/examples/NASA/NASA_SMAP_SPL4SMGP_007.py
new file mode 100644
index 000000000..e537b87f9
--- /dev/null
+++ b/examples/NASA/NASA_SMAP_SPL4SMGP_007.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('NASA/SMAP/SPL4SMGP/007').filter(
+    ee.Filter.date('2017-04-01', '2017-04-30')
+)
+
+sm_surface = dataset.select('sm_surface')
+
+sm_surface_vis = {
+    'min': 0.0,
+    'max': 0.9,
+    'palette': ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
+}
+
+m = geemap.Map()
+m.set_center(-6.746, 46.529, 2)
+m.add_layer(sm_surface, sm_surface_vis, 'SM Surface')
+m
diff --git a/examples/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.py b/examples/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.py
new file mode 100644
index 000000000..3e02583fa
--- /dev/null
+++ b/examples/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture').filter(
+    ee.Filter.date('2017-04-01', '2017-04-30')
+)
+
+soil_moisture = dataset.select('ssm')
+
+soil_moisture_vis = {
+    'min': 0.0,
+    'max': 28.0,
+    'palette': ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
+}
+
+m = geemap.Map()
+m.set_center(-6.746, 46.529, 2)
+m.add_layer(soil_moisture, soil_moisture_vis, 'Soil Moisture')
+m
diff --git a/examples/NOAA/NOAA_CDR_AVHRR_NDVI_V5.py b/examples/NOAA/NOAA_CDR_AVHRR_NDVI_V5.py
new file mode 100644
index 000000000..4035c447f
--- /dev/null
+++ b/examples/NOAA/NOAA_CDR_AVHRR_NDVI_V5.py
@@ -0,0 +1,25 @@
+dataset = ee.ImageCollection('NOAA/CDR/AVHRR/NDVI/V5').filter(
+    ee.Filter.date('2018-05-01', '2018-06-01')
+)
+
+ndvi = dataset.select('NDVI')
+
+ndvi_vis = {
+    'min': -1000.0,
+    'max': 5000.0,
+    'palette': [
+        'ffffff',
+        'ce7e45',
+        'fcd163',
+        'c6ca02',
+        '22cc04',
+        '99b718',
+        '207401',
+        '012e01',
+    ],
+}
+
+m = geemap.Map()
+m.set_center(7.71, 17.93, 2)
+m.add_layer(ndvi, ndvi_vis, 'NDVI')
+m
diff --git a/examples/NOAA/NOAA_CFSV2_FOR6H.py b/examples/NOAA/NOAA_CFSV2_FOR6H.py
new file mode 100644
index 000000000..b13b4a512
--- /dev/null
+++ b/examples/NOAA/NOAA_CFSV2_FOR6H.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('NOAA/CFSV2/FOR6H').filter(
+    ee.Filter.date('2018-03-01', '2018-03-14')
+)
+
+temperature_above_ground = dataset.select('Temperature_height_above_ground')
+
+vis_params = {
+    'min': 220.0,
+    'max': 310.0,
+    'palette': ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.set_center(-88.6, 26.4, 1)
+m.add_layer(temperature_above_ground, vis_params, 'Temperature Above Ground')
+m
diff --git a/examples/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.py b/examples/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.py
new file mode 100644
index 000000000..e27213b78
--- /dev/null
+++ b/examples/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS').filter(
+    ee.Filter.date('2010-01-01', '2010-12-31')
+)
+
+nighttime_lights = dataset.select('avg_vis')
+
+nighttime_lights_vis = {'min': 3.0, 'max': 60.0}
+
+m = geemap.Map()
+m.set_center(7.82, 49.1, 4)
+m.add_layer(nighttime_lights, nighttime_lights_vis, 'Nighttime Lights')
+m
diff --git a/examples/NOAA/NOAA_GFS0P25.py b/examples/NOAA/NOAA_GFS0P25.py
new file mode 100644
index 000000000..55e8d5fc8
--- /dev/null
+++ b/examples/NOAA/NOAA_GFS0P25.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('NOAA/GFS0P25').filter(
+    ee.Filter.date('2018-03-01', '2018-03-02')
+)
+
+temperature_above_ground = dataset.select('temperature_2m_above_ground')
+
+vis_params = {
+    'min': -40.0,
+    'max': 35.0,
+    'palette': ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
+}
+
+m = geemap.Map()
+m.set_center(71.72, 52.48, 3.0)
+m.add_layer(temperature_above_ground, vis_params, 'Temperature Above Ground')
+m
diff --git a/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG.py b/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG.py
new file mode 100644
index 000000000..8f3999c27
--- /dev/null
+++ b/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG').filter(
+    ee.Filter.date('2017-05-01', '2017-05-31')
+)
+
+nighttime = dataset.select('avg_rad')
+
+nighttime_vis = {'min': 0.0, 'max': 60.0}
+
+m = geemap.Map()
+m.set_center(-77.1056, 38.8904, 8)
+m.add_layer(nighttime, nighttime_vis, 'Nighttime')
+m
diff --git a/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.py b/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.py
new file mode 100644
index 000000000..79bed4701
--- /dev/null
+++ b/examples/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG').filter(
+    ee.Filter.date('2017-05-01', '2017-05-31')
+)
+
+nighttime = dataset.select('avg_rad')
+
+nighttime_vis = {'min': 0.0, 'max': 60.0}
+
+m = geemap.Map()
+m.set_center(-77.1056, 38.8904, 8)
+m.add_layer(nighttime, nighttime_vis, 'Nighttime')
+m
diff --git a/examples/RESOLVE/RESOLVE_ECOREGIONS_2017.py b/examples/RESOLVE/RESOLVE_ECOREGIONS_2017.py
new file mode 100644
index 000000000..1573b08e1
--- /dev/null
+++ b/examples/RESOLVE/RESOLVE_ECOREGIONS_2017.py
@@ -0,0 +1,42 @@
+eco_regions = ee.FeatureCollection('RESOLVE/ECOREGIONS/2017')
+
+# patch updated colors
+color_updates = [
+    {'ECO_ID': 204, 'COLOR': '#B3493B'},
+    {'ECO_ID': 245, 'COLOR': '#267400'},
+    {'ECO_ID': 259, 'COLOR': '#004600'},
+    {'ECO_ID': 286, 'COLOR': '#82F178'},
+    {'ECO_ID': 316, 'COLOR': '#E600AA'},
+    {'ECO_ID': 453, 'COLOR': '#5AA500'},
+    {'ECO_ID': 317, 'COLOR': '#FDA87F'},
+    {'ECO_ID': 763, 'COLOR': '#A93800'},
+]
+
+# loop over all other features and create a new style property for styling
+# later on
+eco_regions = eco_regions.map(
+    lambda f: f.set({'style': {'color': f.get('COLOR'), 'width': 0}})
+)
+
+# make styled features for the regions we need to update colors for,
+# then strip them from the main asset and merge in the new feature
+for i in range(len(color_updates)):
+  color_updates[i]['layer'] = eco_regions.filterMetadata(
+      'ECO_ID', 'equals', color_updates[i]['ECO_ID']
+  ).map(
+      lambda f: f.set(
+          {'style': {'color': color_updates[i]['COLOR'], 'width': 0}}
+      )
+  )
+
+  eco_regions = eco_regions.filterMetadata(
+      'ECO_ID', 'not_equals', color_updates[i]['ECO_ID']
+  ).merge(color_updates[i]['layer'])
+
+# use style property to color shapes
+image_rgb = eco_regions.style(styleProperty='style')
+
+m = geemap.Map()
+m.set_center(16, 49, 4)
+m.add_layer(image_rgb, {}, 'RESOLVE/ECOREGIONS/2017')
+m
diff --git a/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL.py b/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL.py
new file mode 100644
index 000000000..23ff9ae16
--- /dev/null
+++ b/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL.py
@@ -0,0 +1,10 @@
+dataset = ee.ImageCollection('SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL')
+
+false_color = dataset.select(['N', 'G', 'B'])
+
+false_color_vis = {'min': 200.0, 'max': 6000.0}
+
+m = geemap.Map()
+m.set_center(-70.892, 41.6555, 15)
+m.add_layer(false_color, false_color_vis, 'False Color')
+m
diff --git a/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB.py b/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB.py
new file mode 100644
index 000000000..59cfec172
--- /dev/null
+++ b/examples/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB.py
@@ -0,0 +1,10 @@
+dataset = ee.ImageCollection('SKYSAT/GEN-A/PUBLIC/ORTHO/RGB')
+
+rgb = dataset.select(['R', 'G', 'B'])
+
+rgb_vis = {'min': 11.0, 'max': 190.0}
+
+m = geemap.Map()
+m.set_center(-70.892, 41.6555, 15)
+m.add_layer(rgb, rgb_vis, 'RGB')
+m
diff --git a/examples/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD.py b/examples/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD.py
new file mode 100644
index 000000000..1c6fffbd8
--- /dev/null
+++ b/examples/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD.py
@@ -0,0 +1,16 @@
+dataset = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').filter(
+    ee.Filter.date('2018-05-01', '2018-05-05')
+)
+
+precipitation = dataset.select('precipitation')
+
+precipitation_vis = {
+    'min': 0,
+    'max': 112,
+    'palette': ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000'],
+}
+
+m = geemap.Map()
+m.set_center(17.93, 7.71, 2)
+m.add_layer(precipitation, precipitation_vis, 'Precipitation')
+m
diff --git a/examples/UMD/UMD_hansen_global_forest_change_2021_v1_9.py b/examples/UMD/UMD_hansen_global_forest_change_2021_v1_9.py
new file mode 100644
index 000000000..6c6782741
--- /dev/null
+++ b/examples/UMD/UMD_hansen_global_forest_change_2021_v1_9.py
@@ -0,0 +1,20 @@
+dataset = ee.Image('UMD/hansen/global_forest_change_2021_v1_9')
+
+tree_cover_vis_param = {
+    'bands': ['treecover2000'],
+    'min': 0,
+    'max': 100,
+    'palette': ['black', 'green'],
+}
+
+tree_loss_vis_param = {
+    'bands': ['lossyear'],
+    'min': 0,
+    'max': 21,
+    'palette': ['yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(dataset, tree_cover_vis_param, 'tree cover')
+m.add_layer(dataset, tree_loss_vis_param, 'tree loss year')
+m
diff --git a/examples/UMD/UMD_hansen_global_forest_change_2022_v1_10.py b/examples/UMD/UMD_hansen_global_forest_change_2022_v1_10.py
new file mode 100644
index 000000000..0c6083613
--- /dev/null
+++ b/examples/UMD/UMD_hansen_global_forest_change_2022_v1_10.py
@@ -0,0 +1,20 @@
+dataset = ee.Image('UMD/hansen/global_forest_change_2022_v1_10')
+
+tree_cover_vis_param = {
+    'bands': ['treecover2000'],
+    'min': 0,
+    'max': 100,
+    'palette': ['black', 'green'],
+}
+
+tree_loss_vis_param = {
+    'bands': ['lossyear'],
+    'min': 0,
+    'max': 22,
+    'palette': ['yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(dataset, tree_cover_vis_param, 'tree cover')
+m.add_layer(dataset, tree_loss_vis_param, 'tree loss year')
+m
diff --git a/examples/UMD/UMD_hansen_global_forest_change_2023_v1_11.py b/examples/UMD/UMD_hansen_global_forest_change_2023_v1_11.py
new file mode 100644
index 000000000..db20878b7
--- /dev/null
+++ b/examples/UMD/UMD_hansen_global_forest_change_2023_v1_11.py
@@ -0,0 +1,20 @@
+dataset = ee.Image('UMD/hansen/global_forest_change_2023_v1_11')
+
+tree_cover_vis_param = {
+    'bands': ['treecover2000'],
+    'min': 0,
+    'max': 100,
+    'palette': ['black', 'green'],
+}
+
+tree_loss_vis_param = {
+    'bands': ['lossyear'],
+    'min': 0,
+    'max': 23,
+    'palette': ['yellow', 'red'],
+}
+
+m = geemap.Map()
+m.add_layer(dataset, tree_cover_vis_param, 'tree cover')
+m.add_layer(dataset, tree_loss_vis_param, 'tree loss year')
+m
diff --git a/examples/USDA/USDA_NAIP_DOQQ.py b/examples/USDA/USDA_NAIP_DOQQ.py
new file mode 100644
index 000000000..4a7d75b61
--- /dev/null
+++ b/examples/USDA/USDA_NAIP_DOQQ.py
@@ -0,0 +1,12 @@
+dataset = ee.ImageCollection('USDA/NAIP/DOQQ').filter(
+    ee.Filter.date('2017-01-01', '2018-12-31')
+)
+
+true_color = dataset.select(['R', 'G', 'B'])
+
+true_color_vis = {'min': 0, 'max': 255}
+
+m = geemap.Map()
+m.set_center(-73.9958, 40.7278, 15)
+m.add_layer(true_color, true_color_vis, 'True Color')
+m
diff --git a/examples/USDA/USDA_NASS_CDL.py b/examples/USDA/USDA_NASS_CDL.py
new file mode 100644
index 000000000..731902db7
--- /dev/null
+++ b/examples/USDA/USDA_NASS_CDL.py
@@ -0,0 +1,12 @@
+dataset = (
+    ee.ImageCollection('USDA/NASS/CDL')
+    .filter(ee.Filter.date('2018-01-01', '2019-12-31'))
+    .first()
+)
+
+crop_landcover = dataset.select('cropland')
+
+m = geemap.Map()
+m.set_center(-100.55, 40.71, 4)
+m.add_layer(crop_landcover, {}, 'Crop Landcover')
+m
diff --git a/examples/USDOS/USDOS_LSIB_SIMPLE_2017.py b/examples/USDOS/USDOS_LSIB_SIMPLE_2017.py
new file mode 100644
index 000000000..eab8daee9
--- /dev/null
+++ b/examples/USDOS/USDOS_LSIB_SIMPLE_2017.py
@@ -0,0 +1,8 @@
+dataset = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')
+
+countries = dataset.style(fillColor='b5ffb4', color='00909F', width=3)
+
+m = geemap.Map()
+m.set_center(16.35, 48.83, 4)
+m.add_layer(countries, {}, 'USDOS/LSIB_SIMPLE/2017', True, 0.8)
+m
diff --git a/examples/USGS/USGS_GFSAD1000_V1.py b/examples/USGS/USGS_GFSAD1000_V1.py
new file mode 100644
index 000000000..3e0a34011
--- /dev/null
+++ b/examples/USGS/USGS_GFSAD1000_V1.py
@@ -0,0 +1,14 @@
+dataset = ee.Image('USGS/GFSAD1000_V1')
+
+crop_mask = dataset.select('landcover')
+
+crop_mask_vis = {
+    'min': 0.0,
+    'max': 5.0,
+    'palette': ['black', 'orange', 'brown', '02a50f', 'green', 'yellow'],
+}
+
+m = geemap.Map()
+m.set_center(-17.22, 13.72, 2)
+m.add_layer(crop_mask, crop_mask_vis, 'Crop Mask')
+m
diff --git a/examples/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.py b/examples/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.py
new file mode 100644
index 000000000..49b77bec2
--- /dev/null
+++ b/examples/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.py
@@ -0,0 +1,20 @@
+# Import the NLCD collection.
+dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD')
+
+# The collection contains images for multiple years and regions in the USA.
+display('Products:', dataset.aggregate_array('system:index'))
+
+# Filter the collection to the 2016 product.
+nlcd_2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()
+
+# Each product has multiple bands for describing aspects of land cover.
+display('Bands:', nlcd_2016.bandNames())
+
+# Select the land cover band.
+landcover = nlcd_2016.select('landcover')
+
+# Display land cover on the map.
+m = geemap.Map()
+m.set_center(-95, 38, 5)
+m.add_layer(landcover, None, 'Landcover')
+m
diff --git a/examples/USGS/USGS_SRTMGL1_003.py b/examples/USGS/USGS_SRTMGL1_003.py
new file mode 100644
index 000000000..dd9d13ac8
--- /dev/null
+++ b/examples/USGS/USGS_SRTMGL1_003.py
@@ -0,0 +1,8 @@
+dataset = ee.Image('USGS/SRTMGL1_003')
+elevation = dataset.select('elevation')
+slope = ee.Terrain.slope(elevation)
+
+m = geemap.Map()
+m.set_center(-112.8598, 36.2841, 10)
+m.add_layer(slope, {'min': 0, 'max': 60}, 'slope')
+m
diff --git a/examples/WorldPop/WorldPop_GP_100m_pop.py b/examples/WorldPop/WorldPop_GP_100m_pop.py
new file mode 100644
index 000000000..521f9f778
--- /dev/null
+++ b/examples/WorldPop/WorldPop_GP_100m_pop.py
@@ -0,0 +1,13 @@
+dataset = ee.ImageCollection('WorldPop/GP/100m/pop')
+
+visualization = {
+    'bands': ['population'],
+    'min': 0.0,
+    'max': 50.0,
+    'palette': ['24126c', '1fff4f', 'd4ff50'],
+}
+
+m = geemap.Map()
+m.set_center(113.643, 34.769, 7)
+m.add_layer(dataset, visualization, 'Population')
+m