Image/extract_value_to_points.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/Image/extract_value_to_points.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/Image/extract_value_to_points.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/Image/extract_value_to_points.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "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'])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import ee\n",
        "import geemap"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "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. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "# Input imagery is a cloud-free Landsat 8 composite.\n",
        "l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1')\n",
        "\n",
        "image = ee.Algorithms.Landsat.simpleComposite(**{\n",
        "    'collection': l8.filterDate('2018-01-01', '2018-12-31'),\n",
        "    'asFloat': True\n",
        "})\n",
        "\n",
        "# Use these bands for prediction.\n",
        "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11']\n",
        "\n",
        "# Load training points. The numeric property 'class' stores known labels.\n",
        "points = ee.FeatureCollection('GOOGLE/EE/DEMOS/demo_landcover_labels')\n",
        "\n",
        "# This property of the table stores the land cover labels.\n",
        "label = 'landcover'\n",
        "\n",
        "# Overlay the points on the imagery to get training.\n",
        "training = image.select(bands).sampleRegions(**{\n",
        "    'collection': points,\n",
        "    'properties': [label],\n",
        "    'scale': 30\n",
        "})\n",
        "\n",
        "# Define visualization parameters in an object literal.\n",
        "vizParams = {'bands': ['B5', 'B4', 'B3'],\n",
        "             'min': 0, 'max': 1, 'gamma': 1.3}\n",
        "\n",
        "\n",
        "Map.centerObject(points, 10)\n",
        "Map.addLayer(image, vizParams, 'Image')\n",
        "Map.addLayer(points, {'color': \"yellow\"}, 'Training points')\n",
        "\n",
        "first = training.first()\n",
        "print(first.getInfo())\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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