Algorithms/landsat_cloud_score.ipynb

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    "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
    "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/landsat_cloud_score.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
    "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Algorithms/landsat_cloud_score.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
    "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Algorithms/landsat_cloud_score.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
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    "## Install Earth Engine API and geemap\n",
    "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
    "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
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  {
   "cell_type": "code",
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   "source": [
    "# Installs geemap package\n",
    "import subprocess\n",
    "\n",
    "try:\n",
    "    import geemap\n",
    "except ImportError:\n",
    "    print('Installing geemap ...')\n",
    "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an interactive map \n",
    "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map(center=[40,-100], zoom=4)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Earth Engine Python script "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Add Earth Engine dataset\n",
    "# Load a cloudy Landsat scene and display it.\n",
    "cloudy_scene = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140926')\n",
    "Map.centerObject(cloudy_scene)\n",
    "Map.addLayer(cloudy_scene, {'bands': ['B4', 'B3', 'B2'], 'max': 0.4}, 'TOA', False)\n",
    "\n",
    "# Add a cloud score band.  It is automatically called 'cloud'.\n",
    "scored = ee.Algorithms.Landsat.simpleCloudScore(cloudy_scene)\n",
    "\n",
    "# Create a mask from the cloud score and combine it with the image mask.\n",
    "mask = scored.select(['cloud']).lte(20)\n",
    "\n",
    "# Apply the mask to the image and display the result.\n",
    "masked = cloudy_scene.updateMask(mask)\n",
    "Map.addLayer(masked, {'bands': ['B4', 'B3', 'B2'], 'max': 0.4}, 'masked')\n",
    "\n",
    "# Load a Landsat 8 composite and set the SENSOR_ID property.\n",
    "mosaic = ee.Image(ee.ImageCollection('LANDSAT/LC8_L1T_8DAY_TOA').first()) \\\n",
    "  .set('SENSOR_ID', 'OLI_TIRS')\n",
    "\n",
    "# Cloud score the mosaic and display the result.\n",
    "scored_mosaic = ee.Algorithms.Landsat.simpleCloudScore(mosaic)\n",
    "Map.addLayer(scored_mosaic, {'bands': ['B4', 'B3', 'B2'], 'max': 0.4},\n",
    "    'TOA mosaic', False)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Earth Engine data layers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
    "Map"
   ]
  }
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