diff --git a/docs/code/python_dataviz/10_leafmap_basics.ipynb b/docs/code/python_dataviz/10_leafmap_basics.ipynb
index fca261a0..90667ead 100644
--- a/docs/code/python_dataviz/10_leafmap_basics.ipynb
+++ b/docs/code/python_dataviz/10_leafmap_basics.ipynb
@@ -1,392 +1,392 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fd708770-59b8-4fb9-acce-2b2d837d88e6",
-   "metadata": {
-    "id": "fd708770-59b8-4fb9-acce-2b2d837d88e6"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends. \n",
-    "\n",
-    "We will explore the capabilities of Leafmap and create a map that includes vector and raster layers. For a more comprehensive overview, check out [leafmap key Features](https://leafmap.org/notebooks/00_key_features/)."
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "fd708770-59b8-4fb9-acce-2b2d837d88e6",
+      "metadata": {
+        "id": "fd708770-59b8-4fb9-acce-2b2d837d88e6"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends.\n",
+        "\n",
+        "We will explore the capabilities of Leafmap and create a map that includes vector and raster layers. For a more comprehensive overview, check out [leafmap key Features](https://leafmap.org/notebooks/00_key_features/)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3LKOmTSadQCJ",
+      "metadata": {
+        "id": "3LKOmTSadQCJ"
+      },
+      "source": [
+        "## Setup and Data Download"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "QYAcw2IjHVzi",
+      "metadata": {
+        "id": "QYAcw2IjHVzi"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "if 'google.colab' in str(get_ipython()):\n",
+        "  !apt install libspatialindex-dev\n",
+        "  !pip install fiona shapely pyproj rtree mapclassify\n",
+        "  !pip install geopandas\n",
+        "  !pip install leafmap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "df487246-fb38-451f-a76d-ffbb7ad95a0f",
+      "metadata": {
+        "id": "df487246-fb38-451f-a76d-ffbb7ad95a0f"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import geopandas as gpd\n",
+        "import leafmap.foliumap as leafmap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "lOLD5d1yHZIC",
+      "metadata": {
+        "id": "lOLD5d1yHZIC"
+      },
+      "outputs": [],
+      "source": [
+        "data_folder = 'data'\n",
+        "output_folder = 'output'\n",
+        "\n",
+        "if not os.path.exists(data_folder):\n",
+        "    os.mkdir(data_folder)\n",
+        "if not os.path.exists(output_folder):\n",
+        "    os.mkdir(output_folder)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1PVxJlqkHb0q",
+      "metadata": {
+        "id": "1PVxJlqkHb0q"
+      },
+      "outputs": [],
+      "source": [
+        "def download(url):\n",
+        "    filename = os.path.join(data_folder, os.path.basename(url))\n",
+        "    if not os.path.exists(filename):\n",
+        "        from urllib.request import urlretrieve\n",
+        "        local, _ = urlretrieve(url, filename)\n",
+        "        print('Downloaded ' + local)\n",
+        "\n",
+        "json_file = 'bangalore_wards.json'\n",
+        "gpkg_file = 'bangalore_roads.gpkg'\n",
+        "\n",
+        "data_url = 'https://github.com/spatialthoughts/python-dataviz-web/raw/main/data/bangalore/'\n",
+        "\n",
+        "for f in json_file, gpkg_file:\n",
+        "  download(data_url + f)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "z_PRvY7aiq6Z",
+      "metadata": {
+        "id": "z_PRvY7aiq6Z"
+      },
+      "source": [
+        "## Creating a Map"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9fEqEt0_iuYR",
+      "metadata": {
+        "id": "9fEqEt0_iuYR"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "50cY2MDqf0HH",
+      "metadata": {
+        "id": "50cY2MDqf0HH"
+      },
+      "source": [
+        "You can change the basemap to a Google basemap and set the center of the map. `leafmap.Map()` supports many arguments to customize the map and available controls.\n",
+        "\n",
+        "References:\n",
+        "\n",
+        "* [`leafmap.leafmap.Map`](https://leafmap.org/leafmap/#leafmap.leafmap.Map)\n",
+        "* [`ipyleaflet.leaflet.Map`](https://ipyleaflet.readthedocs.io/en/latest/api_reference/index.html#ipyleaflet.leaflet.Map)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b-q1fHrTf85g",
+      "metadata": {
+        "id": "b-q1fHrTf85g"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500, google_map='HYBRID', center=(12.97, 77.59), zoom=10)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "XJD3xD3ZtXxD",
+      "metadata": {
+        "id": "XJD3xD3ZtXxD"
+      },
+      "source": [
+        "## Adding Data Layers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8NyMffsKkN3L",
+      "metadata": {
+        "id": "8NyMffsKkN3L"
+      },
+      "source": [
+        "Leafmap's `foliummap` module supports adding a variety of data types along with helper functions such as `set_center()`. Let's add a GeoJSON file to the map using `add_geojson()`.\n",
+        "\n",
+        "Reference: [leafmap.foliumap.Map.add_geojson](https://leafmap.org/foliumap/#leafmap.foliumap.Map.add_geojson)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fjBPneGOjTWB",
+      "metadata": {
+        "id": "fjBPneGOjTWB"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "\n",
+        "json_filepath = os.path.join(data_folder, json_file)\n",
+        "\n",
+        "m.add_geojson(json_filepath, layer_name='City')\n",
+        "m.set_center(77.59, 12.97, 10)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "D3NQmnP3hf0v",
+      "metadata": {
+        "id": "D3NQmnP3hf0v"
+      },
+      "source": [
+        "We can also add any vector layer that can be read by GeoPandas. Here we add a line layer from a GeoPackage using the `add_gdf()` function. The styling parameters can be any folium supported styles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "gaJ5zMffkeHP",
+      "metadata": {
+        "id": "gaJ5zMffkeHP"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "\n",
+        "gpkg_filepath = os.path.join(data_folder, gpkg_file)\n",
+        "\n",
+        "roads_gdf = gpd.read_file(gpkg_filepath)\n",
+        "\n",
+        "m.add_gdf(roads_gdf, layer_name='Roads', style={'color':'blue', 'weight':0.5})\n",
+        "m.zoom_to_gdf(roads_gdf)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "oazyce9GiJxn",
+      "metadata": {
+        "id": "oazyce9GiJxn"
+      },
+      "source": [
+        "A very useful feature of LeafMap is the ability to load a Cloud-Optimized GeoTIFF (COG) file directly from a URL without the need of a server. The file is streamed directly and high-resolution tiles are automatically fetched as you zoom in. We load a 8-bit RGB image hosted on GitHub using the `add_cog_layer()` function.\n",
+        "\n",
+        "Reference: [`leafmap.Map.add_cog_layer`](https://leafmap.org/leafmap/#leafmap.leafmap.Map.add_cog_layer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "maq-ro4rl_Pj",
+      "metadata": {
+        "id": "maq-ro4rl_Pj"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "\n",
+        "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
+        "bounds = leafmap.cog_bounds(cog_url)\n",
+        "\n",
+        "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
+        "m.zoom_to_bounds(bounds)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Z6ajwt53tfzY",
+      "metadata": {
+        "id": "Z6ajwt53tfzY"
+      },
+      "source": [
+        "Leafmap also supports adding custom legends. We define a list of color codes and labels for the land cover classes contained in the `bangalore_lulc.tif` image. We can now add a legend to the map using the `add_legend()` function.\n",
+        "\n",
+        "Reference: [leafmap.foliumap.Map.add_legend](https://leafmap.org/foliumap/#leafmap.foliumap.Map.add_legend)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "F02jMssOq2tC",
+      "metadata": {
+        "id": "F02jMssOq2tC"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "\n",
+        "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
+        "bounds = leafmap.cog_bounds(cog_url)\n",
+        "\n",
+        "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
+        "m.zoom_to_bounds(bounds)\n",
+        "\n",
+        "# Add a Legend\n",
+        "colors = ['#006400', '#ffbb22','#ffff4c','#f096ff','#fa0000',\n",
+        "          '#b4b4b4','#f0f0f0','#0064c8','#0096a0','#00cf75','#fae6a0']\n",
+        "labels = [\"Trees\",\"Shrubland\",\"Grassland\",\"Cropland\",\"Built-up\",\n",
+        "          \"Barren / sparse vegetation\",\"Snow and ice\",\"Open water\",\n",
+        "          \"Herbaceous wetland\",\"Mangroves\",\"Moss and lichen\"]\n",
+        "m.add_legend(colors=colors, labels=labels)\n",
+        "m"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "WsVirLa2d5rY",
+      "metadata": {
+        "id": "WsVirLa2d5rY"
+      },
+      "source": [
+        "We can save the resulting map to a HTML file using the `to_html()` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "wOkm3hN6dcQD",
+      "metadata": {
+        "id": "wOkm3hN6dcQD"
+      },
+      "outputs": [],
+      "source": [
+        "m = leafmap.Map(width=800, height=500)\n",
+        "\n",
+        "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
+        "bounds = leafmap.cog_bounds(cog_url)\n",
+        "\n",
+        "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
+        "m.zoom_to_bounds(bounds)\n",
+        "\n",
+        "# Add a Legend\n",
+        "colors = ['#006400', '#ffbb22','#ffff4c','#f096ff','#fa0000',\n",
+        "          '#b4b4b4','#f0f0f0','#0064c8','#0096a0','#00cf75','#fae6a0']\n",
+        "labels = [\"Trees\",\"Shrubland\",\"Grassland\",\"Cropland\",\"Built-up\",\n",
+        "          \"Barren / sparse vegetation\",\"Snow and ice\",\"Open water\",\n",
+        "          \"Herbaceous wetland\",\"Mangroves\",\"Moss and lichen\"]\n",
+        "m.add_legend(colors=colors, labels=labels)\n",
+        "\n",
+        "output_file = 'lulc.html'\n",
+        "output_path = os.path.join(output_folder, output_file)\n",
+        "m.to_html(output_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "852P-W2Kc3q6",
+      "metadata": {
+        "id": "852P-W2Kc3q6"
+      },
+      "source": [
+        "## Exercise\n",
+        "\n",
+        "The code below contains a basic leafmap map. We want to a raster layers of VIIRS Nighttime Lights over India. The URL to a Cloud Optmized GeoTiff (COG) file hosted on Google Cloud Storage is given below.\n",
+        "\n",
+        "Add the data to the map and visualize it.\n",
+        "\n",
+        "Reference: [`leafmap.Map.add_cog_layer`](https://leafmap.org/leafmap/#leafmap.leafmap.Map.add_cog_layer)\n",
+        "\n",
+        "You will need to specify additional `kwargs` parameters to create a correct visualization.\n",
+        "\n",
+        "1. The GeoTIFF image is a single-band image with grayscale values of night light intensities. The range of these values are between 0-60. Use `rescale='0,60'`.\n",
+        "2. The image has a nodata values stored as `nan`. Use `nodata='nan'`.\n",
+        "3. A grayscale image can be displayed in color using a colormap. Use `colormap_name=viridis`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "sGUkRyUqjAUT",
+      "metadata": {
+        "id": "sGUkRyUqjAUT"
+      },
+      "outputs": [],
+      "source": [
+        "gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/ntl/viirs/'\n",
+        "cog_url = os.path.join(gcs_bucket, 'viirs_ntl_2021_india.tif')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.6"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3LKOmTSadQCJ",
-   "metadata": {
-    "id": "3LKOmTSadQCJ"
-   },
-   "source": [
-    "## Setup and Data Download"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "QYAcw2IjHVzi",
-   "metadata": {
-    "id": "QYAcw2IjHVzi"
-   },
-   "outputs": [],
-   "source": [
-    "%%capture\n",
-    "if 'google.colab' in str(get_ipython()):\n",
-    "  !apt install libspatialindex-dev\n",
-    "  !pip install fiona shapely pyproj rtree mapclassify\n",
-    "  !pip install geopandas\n",
-    "  !pip install leafmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df487246-fb38-451f-a76d-ffbb7ad95a0f",
-   "metadata": {
-    "id": "df487246-fb38-451f-a76d-ffbb7ad95a0f"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import geopandas as gpd\n",
-    "import leafmap.foliumap as leafmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "lOLD5d1yHZIC",
-   "metadata": {
-    "id": "lOLD5d1yHZIC"
-   },
-   "outputs": [],
-   "source": [
-    "data_folder = 'data'\n",
-    "output_folder = 'output'\n",
-    "\n",
-    "if not os.path.exists(data_folder):\n",
-    "    os.mkdir(data_folder)\n",
-    "if not os.path.exists(output_folder):\n",
-    "    os.mkdir(output_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1PVxJlqkHb0q",
-   "metadata": {
-    "id": "1PVxJlqkHb0q"
-   },
-   "outputs": [],
-   "source": [
-    "def download(url):\n",
-    "    filename = os.path.join(data_folder, os.path.basename(url))\n",
-    "    if not os.path.exists(filename):\n",
-    "        from urllib.request import urlretrieve\n",
-    "        local, _ = urlretrieve(url, filename)\n",
-    "        print('Downloaded ' + local)\n",
-    "\n",
-    "json_file = 'bangalore_wards.json'\n",
-    "gpkg_file = 'bangalore_roads.gpkg'\n",
-    "\n",
-    "data_url = 'https://github.com/spatialthoughts/python-dataviz-web/raw/main/data/bangalore/'\n",
-    "\n",
-    "for f in json_file, gpkg_file:\n",
-    "  download(data_url + f)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "z_PRvY7aiq6Z",
-   "metadata": {
-    "id": "z_PRvY7aiq6Z"
-   },
-   "source": [
-    "## Creating a Map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9fEqEt0_iuYR",
-   "metadata": {
-    "id": "9fEqEt0_iuYR"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50cY2MDqf0HH",
-   "metadata": {
-    "id": "50cY2MDqf0HH"
-   },
-   "source": [
-    "You can change the basemap to a Google basemap and set the center of the map. `leafmap.Map()` supports many arguments to customize the map and available controls.\n",
-    "\n",
-    "References: \n",
-    "\n",
-    "* [`leafmap.leafmap.Map`](https://leafmap.org/leafmap/#leafmap.leafmap.Map)\n",
-    "* [`ipyleaflet.leaflet.Map`](https://ipyleaflet.readthedocs.io/en/latest/api_reference/index.html#ipyleaflet.leaflet.Map)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b-q1fHrTf85g",
-   "metadata": {
-    "id": "b-q1fHrTf85g"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500, google_map='HYBRID', center=(12.97, 77.59), zoom=10)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "XJD3xD3ZtXxD",
-   "metadata": {
-    "id": "XJD3xD3ZtXxD"
-   },
-   "source": [
-    "## Adding Data Layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8NyMffsKkN3L",
-   "metadata": {
-    "id": "8NyMffsKkN3L"
-   },
-   "source": [
-    "Leafmap's `foliummap` module supports adding a variety of data types along with helper functions such as `set_center()`. Let's add a GeoJSON file to the map using `add_geojson()`.\n",
-    "\n",
-    "Reference: [leafmap.foliumap.Map.add_geojson](https://leafmap.org/foliumap/#leafmap.foliumap.Map.add_geojson)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fjBPneGOjTWB",
-   "metadata": {
-    "id": "fjBPneGOjTWB"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "\n",
-    "json_filepath = os.path.join(data_folder, json_file)\n",
-    "\n",
-    "m.add_geojson(json_filepath, layer_name='City')\n",
-    "m.set_center(77.59, 12.97, 10)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "D3NQmnP3hf0v",
-   "metadata": {
-    "id": "D3NQmnP3hf0v"
-   },
-   "source": [
-    "We can also add any vector layer that can be read by GeoPandas. Here we add a line layer from a GeoPackage using the `add_gdf()` function. The styling parameters can be any folium supported styles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "gaJ5zMffkeHP",
-   "metadata": {
-    "id": "gaJ5zMffkeHP"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "\n",
-    "gpkg_filepath = os.path.join(data_folder, gpkg_file)\n",
-    "\n",
-    "roads_gdf = gpd.read_file(gpkg_filepath)\n",
-    "\n",
-    "m.add_gdf(roads_gdf, layer_name='Roads', style={'color':'blue', 'weight':0.5})\n",
-    "m.zoom_to_gdf(roads_gdf)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "oazyce9GiJxn",
-   "metadata": {
-    "id": "oazyce9GiJxn"
-   },
-   "source": [
-    "A very useful feature of LeafMap is the ability to load a Cloud-Optimized GeoTIFF (COG) file directly from a URL without the need of a server. The file is streamed directly and high-resolution tiles are automatically fetched as you zoom in. We load a 8-bit RGB image hosted on GitHub using the `add_cog_layer()` function.\n",
-    "\n",
-    "Reference: [`leafmap.Map.add_cog_layer`](https://leafmap.org/leafmap/#leafmap.leafmap.Map.add_cog_layer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "maq-ro4rl_Pj",
-   "metadata": {
-    "id": "maq-ro4rl_Pj"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "\n",
-    "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
-    "bounds = leafmap.cog_bounds(cog_url)\n",
-    "\n",
-    "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
-    "m.zoom_to_bounds(bounds)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "Z6ajwt53tfzY",
-   "metadata": {
-    "id": "Z6ajwt53tfzY"
-   },
-   "source": [
-    "Leafmap also supports adding custom legends. We define a list of color codes and labels for the land cover classes contained in the `bangalore_lulc.tif` image. We can now add a legend to the map using the `add_legend()` function.\n",
-    "\n",
-    "Reference: [leafmap.foliumap.Map.add_legend](https://leafmap.org/foliumap/#leafmap.foliumap.Map.add_legend)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "F02jMssOq2tC",
-   "metadata": {
-    "id": "F02jMssOq2tC"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "\n",
-    "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
-    "bounds = leafmap.cog_bounds(cog_url)\n",
-    "\n",
-    "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
-    "m.zoom_to_bounds(bounds)\n",
-    "\n",
-    "# Add a Legend\n",
-    "colors = ['#006400', '#ffbb22','#ffff4c','#f096ff','#fa0000',\n",
-    "          '#b4b4b4','#f0f0f0','#0064c8','#0096a0','#00cf75','#fae6a0']\n",
-    "labels = [\"Trees\",\"Shrubland\",\"Grassland\",\"Cropland\",\"Built-up\",\n",
-    "          \"Barren / sparse vegetation\",\"Snow and ice\",\"Open water\",\n",
-    "          \"Herbaceous wetland\",\"Mangroves\",\"Moss and lichen\"]\n",
-    "m.add_legend(colors=colors, labels=labels)\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "WsVirLa2d5rY",
-   "metadata": {
-    "id": "WsVirLa2d5rY"
-   },
-   "source": [
-    "We can save the resulting map to a HTML file using the `to_html()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wOkm3hN6dcQD",
-   "metadata": {
-    "id": "wOkm3hN6dcQD"
-   },
-   "outputs": [],
-   "source": [
-    "m = leafmap.Map(width=800, height=500)\n",
-    "\n",
-    "cog_url = os.path.join(data_url, 'bangalore_lulc_rgb.tif')\n",
-    "bounds = leafmap.cog_bounds(cog_url)\n",
-    "\n",
-    "m.add_cog_layer(cog_url, name='Land Use Land Cover')\n",
-    "m.zoom_to_bounds(bounds)\n",
-    "\n",
-    "# Add a Legend\n",
-    "colors = ['#006400', '#ffbb22','#ffff4c','#f096ff','#fa0000',\n",
-    "          '#b4b4b4','#f0f0f0','#0064c8','#0096a0','#00cf75','#fae6a0']\n",
-    "labels = [\"Trees\",\"Shrubland\",\"Grassland\",\"Cropland\",\"Built-up\",\n",
-    "          \"Barren / sparse vegetation\",\"Snow and ice\",\"Open water\",\n",
-    "          \"Herbaceous wetland\",\"Mangroves\",\"Moss and lichen\"]\n",
-    "m.add_legend(colors=colors, labels=labels)\n",
-    "\n",
-    "output_file = 'lulc.html'\n",
-    "output_path = os.path.join(output_folder, output_file)\n",
-    "m.to_html(output_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "852P-W2Kc3q6",
-   "metadata": {
-    "id": "852P-W2Kc3q6"
-   },
-   "source": [
-    "## Exercise\n",
-    "\n",
-    "The code below contains a basic leafmap map. We want to a raster layers of VIIRS Nighttime Lights over India. The URL to a Cloud Optmized GeoTiff (COG) file hosted on Google Cloud Storage is given below.\n",
-    "\n",
-    "Add the data to the map and visualize it.\n",
-    "\n",
-    "Reference: [`leafmap.Map.add_cog_layer`](https://leafmap.org/leafmap/#leafmap.leafmap.Map.add_cog_layer)\n",
-    "\n",
-    "You will need to specify additional `kwargs` parameters to create a correct visualization.\n",
-    "\n",
-    "1. The GeoTIFF image is a single-band image with grayscale values of night light intensities. The range of these values are between 0-60. Use `rescale='0,60'`.\n",
-    "2. The image has a nodata values stored as `nan`. Use `nodata='nan'`.\n",
-    "3. A grayscale image can be displayed in color using a colormap. Use `colormap_name=viridis`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sGUkRyUqjAUT",
-   "metadata": {
-    "id": "sGUkRyUqjAUT"
-   },
-   "outputs": [],
-   "source": [
-    "gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/'\n",
-    "cog_url = os.path.join(gcs_bucket, 'viirs_ntl_2021_india.tif')"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/python-dataviz-output/10_leafmap_basics.md b/docs/python-dataviz-output/10_leafmap_basics.md
index b6eae242..63488525 100644
--- a/docs/python-dataviz-output/10_leafmap_basics.md
+++ b/docs/python-dataviz-output/10_leafmap_basics.md
@@ -1,6 +1,6 @@
 ## Overview
 
-[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends. 
+[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends.
 
 We will explore the capabilities of Leafmap and create a map that includes vector and raster layers. For a more comprehensive overview, check out [leafmap key Features](https://leafmap.org/notebooks/00_key_features/).
 
@@ -63,7 +63,7 @@ m
 
 You can change the basemap to a Google basemap and set the center of the map. `leafmap.Map()` supports many arguments to customize the map and available controls.
 
-References: 
+References:
 
 * [`leafmap.leafmap.Map`](https://leafmap.org/leafmap/#leafmap.leafmap.Map)
 * [`ipyleaflet.leaflet.Map`](https://ipyleaflet.readthedocs.io/en/latest/api_reference/index.html#ipyleaflet.leaflet.Map)
@@ -188,6 +188,6 @@ You will need to specify additional `kwargs` parameters to create a correct visu
 
 
 ```python
-gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/'
+gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/ntl/viirs/'
 cog_url = os.path.join(gcs_bucket, 'viirs_ntl_2021_india.tif')
 ```
diff --git a/docs/python-dataviz.html b/docs/python-dataviz.html
index 70d0b5c7..78e59023 100644
--- a/docs/python-dataviz.html
+++ b/docs/python-dataviz.html
@@ -2577,7 +2577,7 @@ <h2>Exercise</h2>
 <code>colormap_name=viridis</code>.</li>
 </ol>
 <div class="sourceCode" id="cb158"><pre
-class="sourceCode python"><code class="sourceCode python"><span id="cb158-1"><a href="#cb158-1" tabindex="-1"></a>gcs_bucket <span class="op">=</span> <span class="st">&#39;https://storage.googleapis.com/spatialthoughts-public-data/&#39;</span></span>
+class="sourceCode python"><code class="sourceCode python"><span id="cb158-1"><a href="#cb158-1" tabindex="-1"></a>gcs_bucket <span class="op">=</span> <span class="st">&#39;https://storage.googleapis.com/spatialthoughts-public-data/ntl/viirs/&#39;</span></span>
 <span id="cb158-2"><a href="#cb158-2" tabindex="-1"></a>cog_url <span class="op">=</span> os.path.join(gcs_bucket, <span class="st">&#39;viirs_ntl_2021_india.tif&#39;</span>)</span></code></pre></div>
 <div style="page-break-after: always;"></div>
 </div>
diff --git a/python-dataviz-output/10_leafmap_basics.md b/python-dataviz-output/10_leafmap_basics.md
index b6eae242..63488525 100644
--- a/python-dataviz-output/10_leafmap_basics.md
+++ b/python-dataviz-output/10_leafmap_basics.md
@@ -1,6 +1,6 @@
 ## Overview
 
-[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends. 
+[Leafmap](https://leafmap.org/) is a Python package for interactive mapping that supports a wide-variety of plotting backends.
 
 We will explore the capabilities of Leafmap and create a map that includes vector and raster layers. For a more comprehensive overview, check out [leafmap key Features](https://leafmap.org/notebooks/00_key_features/).
 
@@ -63,7 +63,7 @@ m
 
 You can change the basemap to a Google basemap and set the center of the map. `leafmap.Map()` supports many arguments to customize the map and available controls.
 
-References: 
+References:
 
 * [`leafmap.leafmap.Map`](https://leafmap.org/leafmap/#leafmap.leafmap.Map)
 * [`ipyleaflet.leaflet.Map`](https://ipyleaflet.readthedocs.io/en/latest/api_reference/index.html#ipyleaflet.leaflet.Map)
@@ -188,6 +188,6 @@ You will need to specify additional `kwargs` parameters to create a correct visu
 
 
 ```python
-gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/'
+gcs_bucket = 'https://storage.googleapis.com/spatialthoughts-public-data/ntl/viirs/'
 cog_url = os.path.join(gcs_bucket, 'viirs_ntl_2021_india.tif')
 ```