landcover_classification_Maharashtra_(supervised_learning).ipynb

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            "\u001b[?25hCollecting anyio<4,>=3.1.0\n",
            "  Downloading anyio-3.3.4-py3-none-any.whl (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 7.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: argon2-cffi in /usr/local/lib/python3.7/dist-packages (from jupyter-server~=1.4->jupyterlab>=3->geemap) (21.1.0)\n",
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            "Collecting jupyter-client\n",
            "  Downloading jupyter_client-7.0.6-py3-none-any.whl (125 kB)\n",
            "\u001b[K     |████████████████████████████████| 125 kB 70.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from jupyter-server~=1.4->jupyterlab>=3->geemap) (0.12.0)\n",
            "Collecting sniffio>=1.1\n",
            "  Downloading sniffio-1.2.0-py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from anyio<4,>=3.1.0->jupyter-server~=1.4->jupyterlab>=3->geemap) (3.10.0.2)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.7/dist-packages (from anyio<4,>=3.1.0->jupyter-server~=1.4->jupyterlab>=3->geemap) (2.10)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ipyfilechooser>=0.6.0->geemap) (2.8.2)\n",
            "Requirement already satisfied: nest-asyncio>=1.5 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ipyfilechooser>=0.6.0->geemap) (1.5.1)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ipyfilechooser>=0.6.0->geemap) (0.3)\n",
            "Collecting json5\n",
            "  Downloading json5-0.9.6-py2.py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: babel in /usr/local/lib/python3.7/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3->geemap) (2.9.1)\n",
            "Collecting jsonschema>=3.0.1\n",
            "  Downloading jsonschema-4.2.1-py3-none-any.whl (69 kB)\n",
            "\u001b[K     |████████████████████████████████| 69 kB 7.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3->geemap) (5.4.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3->geemap) (21.2.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3->geemap) (0.18.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3->geemap) (4.8.2)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3->geemap) (3.6.0)\n",
            "Requirement already satisfied: notebook<7 in /usr/local/lib/python3.7/dist-packages (from nbclassic~=0.2->jupyterlab>=3->geemap) (5.3.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<2dev,>=1.21.0->google-api-python-client<2,>=1.12.1->earthengine-api>=0.1.230->geemap) (3.0.6)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets->ipyfilechooser>=0.6.0->geemap) (0.2.5)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.4.1->earthengine-api>=0.1.230->geemap) (0.4.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->folium>=0.11.0->geemap) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->folium>=0.11.0->geemap) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->folium>=0.11.0->geemap) (1.24.3)\n",
            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.3->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.7.0)\n",
            "Collecting whitebox\n",
            "  Downloading whitebox-2.0.3-py2.py3-none-any.whl (70 kB)\n",
            "\u001b[K     |████████████████████████████████| 70 kB 9.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cffi>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from argon2-cffi->jupyter-server~=1.4->jupyterlab>=3->geemap) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0.0->argon2-cffi->jupyter-server~=1.4->jupyterlab>=3->geemap) (2.21)\n",
            "Collecting ratelim\n",
            "  Downloading ratelim-0.1.6-py2.py3-none-any.whl (4.0 kB)\n",
            "Requirement already satisfied: google-resumable-media<0.5.0dev,>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage->earthengine-api>=0.1.230->geemap) (0.4.1)\n",
            "Requirement already satisfied: google-cloud-core<2.0dev,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage->earthengine-api>=0.1.230->geemap) (1.0.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->geemap) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->geemap) (1.3.2)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (1.5.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.5.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.7.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (4.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.5.1)\n",
            "Collecting pyproj>=2\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 69.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from owslib->geemap) (3.13)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from sankee->geemap) (4.4.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->sankee->geemap) (1.3.3)\n",
            "Collecting jupyter-client\n",
            "  Downloading jupyter_client-6.1.12-py3-none-any.whl (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 19.4 MB/s \n",
            "\u001b[?25hCollecting nbconvert\n",
            "  Downloading nbconvert-6.3.0-py3-none-any.whl (556 kB)\n",
            "\u001b[K     |████████████████████████████████| 556 kB 60.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: nbclient<0.6,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from voila->geemap) (0.5.8)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3->geemap) (0.1.2)\n",
            "Building wheels for collected packages: folium, pyshp, ipynb-py-convert, pycrs\n",
            "  Building wheel for folium (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for folium: filename=folium-0.12.1.post1-py2.py3-none-any.whl size=95017 sha256=72e8fe82159bae48f86e87aeb79339d8a9b67d874949f22852a4bb69507e74f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/cf/d2/b1/569dd3ad4959f691dbd11987ac20ee2d5d58cb7d9f45968cae\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-2.1.3-py3-none-any.whl size=37325 sha256=dc982b8cc20c47769135978c9c22d0c0f708f24dc05beca23bed1a5d1f1c5904\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/f8/87/53c8cd41545ba20e536ea29a8fcb5431b5f477ca50d5dffbbe\n",
            "  Building wheel for ipynb-py-convert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipynb-py-convert: filename=ipynb_py_convert-0.4.6-py3-none-any.whl size=4639 sha256=7f303b1a59f7f99ba1e04a44fb2863010579bc5ec17ad40aeadd79dbb8428db3\n",
            "  Stored in directory: /root/.cache/pip/wheels/af/31/a9/761b134adbbca3c92d491eff3bad785c0a0c0079695d6f0504\n",
            "  Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycrs: filename=PyCRS-1.0.2-py3-none-any.whl size=32704 sha256=b1755d13c89953ba14ea941801dcd920a63d24a3bb407d73feb6d9c445bfcbfd\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/ce/32/1ec0aba6b9770681a423e82f0274c57d09ad2c20c2864901f9\n",
            "Successfully built folium pyshp ipynb-py-convert pycrs\n",
            "Installing collected packages: tornado, jsonschema, jupyter-client, nbconvert, sniffio, websocket-client, anyio, soupsieve, jupyter-server, json5, whitebox, traittypes, ratelim, pyproj, nbclassic, logzero, jupyterlab-server, ipytree, ipyfilechooser, beautifulsoup4, xyzservices, whiteboxgui, voila, sankee, python-box, pyshp, pycrs, owslib, mss, jupyterlab, ipynb-py-convert, ipyleaflet, ipyevents, geojson, geocoder, geeadd, folium, ffmpeg-python, colour, bqplot, geemap\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
            "      Successfully uninstalled jsonschema-2.6.0\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: nbconvert\n",
            "    Found existing installation: nbconvert 5.6.1\n",
            "    Uninstalling nbconvert-5.6.1:\n",
            "      Successfully uninstalled nbconvert-5.6.1\n",
            "  Attempting uninstall: beautifulsoup4\n",
            "    Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "  Attempting uninstall: folium\n",
            "    Found existing installation: folium 0.8.3\n",
            "    Uninstalling folium-0.8.3:\n",
            "      Successfully uninstalled folium-0.8.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.12.1.post1 which is incompatible.\u001b[0m\n",
            "Successfully installed anyio-3.3.4 beautifulsoup4-4.10.0 bqplot-0.12.31 colour-0.1.5 ffmpeg-python-0.2.0 folium-0.12.1.post1 geeadd-0.5.5 geemap-0.9.4 geocoder-1.38.1 geojson-2.5.0 ipyevents-2.0.1 ipyfilechooser-0.6.0 ipyleaflet-0.14.0 ipynb-py-convert-0.4.6 ipytree-0.2.1 json5-0.9.6 jsonschema-4.2.1 jupyter-client-6.1.12 jupyter-server-1.11.2 jupyterlab-3.2.4 jupyterlab-server-2.8.2 logzero-1.7.0 mss-6.1.0 nbclassic-0.3.4 nbconvert-6.3.0 owslib-0.25.0 pycrs-1.0.2 pyproj-3.2.1 pyshp-2.1.3 python-box-5.4.1 ratelim-0.1.6 sankee-0.0.5 sniffio-1.2.0 soupsieve-2.3.1 tornado-6.1 traittypes-0.2.1 voila-0.2.16 websocket-client-1.2.1 whitebox-2.0.3 whiteboxgui-0.6.0 xyzservices-2021.11.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "jupyter_client",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zIxMf9z1pHR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "70b7e917-03b3-4a95-fd37-ad3188022b74"
      },
      "source": [
        "import ee\n",
        "import geemap\n",
        "# Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the library.\n",
        "ee.Initialize()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://accounts.google.com/o/oauth2/auth?client_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fearthengine+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&code_challenge=bd5GxS2_0OrBgJcrsqLKoJ3XqN_AEKZZ_aZnrQ8jx-k&code_challenge_method=S256\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/1AX4XfWjRcIoSFc2K1pWzv4D1cE6YaM6MxLmediDLjIM7NgYG4-rB-EKlOX0\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NivFnVAb1v83"
      },
      "source": [
        "Map = geemap.Map()\n",
        "\n",
        "admin = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level1\");\n",
        "maharashtra = admin.filter(ee.Filter.eq('ADM1_NAME', 'Maharashtra'))\n",
        "geometry = maharashtra.geometry()\n",
        "\n",
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "\n",
        "image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \\\n",
        "    .filterDate('2019-01-01', '2019-12-31') \\\n",
        "    .sort('CLOUD_COVER') \\\n",
        "    .min() \\\n",
        "    .select('B[1-7]') \\\n",
        "    .clip(geometry)\n",
        "\n",
        "\n",
        "vis_params = {\n",
        "    'min': 0,\n",
        "    'max': 3000,\n",
        "    'bands': ['B5', 'B4', 'B3']\n",
        "}\n",
        "\n",
        "Map.centerObject(point, 5)\n",
        "Map.addLayer(image, vis_params, \"Landsat-8\")\n",
        "\n",
        "#Map"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfZMbkTc10tj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "b71b3e972fd8420fa40ae2df417e671a",
            "3accbde19c6f4ee1b2cde9558240bfba",
            "e799dc4984d74e0685290ea16b0065ed",
            "f964e22885f14f4f8c7b951f9cb842aa",
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            "519179f0435b4978b479b61cd3102618",
            "f2d70834f3d34eb393ab0fd86244b889",
            "d4369e52e65241f4adeabb7c1e9bcb97",
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            "6fefe4fe612849d1b046a58a60b2cf41",
            "fed33dd49d964656b4c3b4b7562c0c4c",
            "b13965464b484ecc8990ae827d97c5c0",
            "7de7e6cc010b45f5b90ac59d0aecbf6b",
            "47c7ee7e43de43f2bdb25436e9d1e3a6",
            "34b5b693600247688fe36e46fe791cc9",
            "ca9f7ce93d024690b25b0c7e13278599",
            "cc5f71bf323b4601b25eba405d1185d5",
            "5f0052fe069d468ea34d1e5b90bb88cf",
            "4ef7d173b8cf459084de3f10d7cb5e86",
            "151aed478f1b4ce4bd2f81aba371e495",
            "f8a8e177c11d498595456c273fe2f9d5",
            "b1181cdde480407d9414f22a467493a1",
            "b80f84afabc24b66b9e074d960646ad1",
            "960eb34ebbbf446690f99a5f5bddd385",
            "fd767d3833244542b613b6304e83cdb8",
            "b1871703abe84ae1a114346dcc6b9a4f",
            "2867dc629a8146ad8a52ee97807dc813",
            "8bd3e6981dfa4533830a6a5ed26d43a4",
            "f7a466a7135448fd878c9f41860bac42",
            "cd1278a681584c0eb8e76580173acee4",
            "1b4d54c5c6064b0388f21d396e9f663e",
            "2f0bc79b333647f3ae4ec4914d34f5dd",
            "25d17b70569442869a5a90507ae37edc",
            "d2afdd67e42f43319bfb4cf12424694a"
          ]
        },
        "outputId": "b508b4c6-73e5-45c9-dd71-0cbfaa630c2a"
      },
      "source": [
        "modis = ee.ImageCollection('MODIS/006/MCD12Q1') \\\n",
        "              .filterDate('2016-01-01', '2019-01-01') \\\n",
        "              .select('LC_Type1') \\\n",
        "              .reduce(ee.Reducer.median())\n",
        "\n",
        "points = modis.sample(**{\n",
        "    'region': ee.Geometry.Rectangle([72.0000, 23.0000, 82.0000, 15.0000]),\n",
        "    'scale': 30,\n",
        "    'numPixels': 5000,\n",
        "    'seed': 0,\n",
        "    'geometries': True  # Set this to False to ignore geometries\n",
        "})\n",
        "\n",
        "Map.addLayer(points, {}, 'training', False)\n",
        "Map"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b71b3e972fd8420fa40ae2df417e671a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JARK8Myc4o1J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b721968-c557-49c1-8d6a-9629c1d5383e"
      },
      "source": [
        "print(points.size().getInfo())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuxCzCLL4q_I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8c29a93-34c6-4dff-d854-f4b88ad5f5e7"
      },
      "source": [
        "print(points.first().getInfo())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [75.96063229451673, 21.19164441922813]}, 'id': '0', 'properties': {'LC_Type1_median': 12}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGxNPIps4ujB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb28ae79-b54a-4e53-a5fb-4c30d982fb03"
      },
      "source": [
        "# Use these bands for prediction.\n",
        "bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']\n",
        "\n",
        "\n",
        "# This property of the table stores the land cover labels.\n",
        "label = 'LC_Type1_median'\n",
        "input = image.select(bands)\n",
        "\n",
        "# Overlay the points on the imagery to get training.\n",
        "training = input.sampleRegions(**{\n",
        "  'collection': points,\n",
        "  'properties': [label],\n",
        "  'scale': 30\n",
        "})\n",
        "\n",
        "# Train a CART classifier with default parameters.\n",
        "trained = ee.Classifier.smileCart().train(training, label, bands)\n",
        "\n",
        "print(training.first().getInfo());"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'type': 'Feature', 'geometry': None, 'id': '0_0', 'properties': {'B1': 282, 'B2': 377, 'B3': 638, 'B4': 548, 'B5': 2163, 'B6': 1395, 'B7': 885, 'LC_Type1_median': 12}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpVJXD_T4usM"
      },
      "source": [
        "result = image.select(bands).classify(trained)\n",
        "\n",
        "# Display the clusters with random colors.\n",
        "# Map.addLayer(result.randomVisualizer(), {}, 'classfied')\n",
        "# Map"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6LcHfGZ5Zal"
      },
      "source": [
        "map2 = geemap.Map()\n",
        "\n",
        "ee_class_table = \"\"\"\n",
        "\n",
        "Value\tColor\tDescription\n",
        "0\t1c0dff\tWater\n",
        "1\t05450a\tEvergreen needleleaf forest\n",
        "2\t086a10\tEvergreen broadleaf forest\n",
        "3\t54a708\tDeciduous needleleaf forest\n",
        "4\t78d203\tDeciduous broadleaf forest\n",
        "5\t009900\tMixed forest\n",
        "6\tc6b044\tClosed shrublands\n",
        "7\tdcd159\tOpen shrublands\n",
        "8\tdade48\tWoody savannas\n",
        "9\tfbff13\tSavannas\n",
        "10\tb6ff05\tGrasslands\n",
        "11\t27ff87\tPermanent wetlands\n",
        "12\tc24f44\tCroplands\n",
        "13\ta5a5a5\tUrban and built-up\n",
        "14\tff6d4c\tCropland/natural vegetation mosaic\n",
        "15\t69fff8\tSnow and ice\n",
        "16\tf9ffa4\tBarren or sparsely vegetated\n",
        "254\tffffff\tUnclassified\n",
        "\n",
        "\"\"\"\n",
        "#############################################################\n",
        "landcover = ee.ImageCollection('MODIS/006/MCD12Q1') \\\n",
        "              .filterDate('2019-01-01', '2019-12-31') \\\n",
        "              .select('LC_Type1') \\\n",
        "              .reduce(ee.Reducer.mean()) \\\n",
        "              .clip(geometry)\n",
        "\n",
        "LandCoverVis = {\n",
        "  'min': 1.0,\n",
        "  'max': 17.0,\n",
        "  'palette': [\n",
        "    '05450a', '086a10', '54a708', '78d203', '009900', 'c6b044', 'dcd159',\n",
        "    'dade48', 'fbff13', 'b6ff05', '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c',\n",
        "    '69fff8', 'f9ffa4', '1c0dff'\n",
        "  ],\n",
        "}\n",
        "\n",
        "Map.centerObject(point, 5)\n",
        "Map.addLayer(landcover, LandCoverVis, 'MODIS Land Cover')\n",
        "\n",
        "legend_dict = geemap.legend_from_ee(ee_class_table)\n",
        "#map2.add_legend(legend_title=\"MODIS Global Land Cover\", legend_dict=legend_dict)\n",
        "#map2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5U8z1xi4-PR"
      },
      "source": [
        "landsatcover = result.set('classification_class_values', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])\n",
        "landsatcover = landsatcover.set('classification_class_palette', ['1c0dff', '05450a', '086a10', '54a708', '78d203', '009900', 'c6b044', 'dcd159', 'dade48', 'fbff13', 'b6ff05', '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c', '69fff8', 'f9ffa4',   ])\n",
        "Map.addLayer(landsatcover, {}, 'Land cover')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6__EKV5o5vC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
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            "2867dc629a8146ad8a52ee97807dc813",
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            "cc5f71bf323b4601b25eba405d1185d5",
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            "cd1278a681584c0eb8e76580173acee4",
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            "1b4d54c5c6064b0388f21d396e9f663e",
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            "d2afdd67e42f43319bfb4cf12424694a"
          ]
        },
        "outputId": "42c8b3a1-1c25-4e20-b9ca-b8a5a4d35c92"
      },
      "source": [
        "ee_class_table = \"\"\"\n",
        "\n",
        "Value\tColor\tDescription\n",
        "0\t1c0dff\tWater\n",
        "1\t05450a\tEvergreen needleleaf forest\n",
        "2\t086a10\tEvergreen broadleaf forest\n",
        "3\t54a708\tDeciduous needleleaf forest\n",
        "4\t78d203\tDeciduous broadleaf forest\n",
        "5\t009900\tMixed forest\n",
        "6\tc6b044\tClosed shrublands\n",
        "7\tdcd159\tOpen shrublands\n",
        "8\tdade48\tWoody savannas\n",
        "9\tfbff13\tSavannas\n",
        "10\tb6ff05\tGrasslands\n",
        "11\t27ff87\tPermanent wetlands\n",
        "12\tc24f44\tCroplands\n",
        "13\ta5a5a5\tUrban and built-up\n",
        "14\tff6d4c\tCropland/natural vegetation mosaic\n",
        "15\t69fff8\tSnow and ice\n",
        "16\tf9ffa4\tBarren or sparsely vegetated\n",
        "254\tffffff\tUnclassified\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "legend_dict = geemap.legend_from_ee(ee_class_table)\n",
        "Map.add_legend(legend_title=\"Land Cover Legend\", legend_dict=legend_dict)\n",
        "\n",
        "Map"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b71b3e972fd8420fa40ae2df417e671a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/e680a8b83b2ea152/manager.min.js"
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          }
        }
      ]
    }
  ]
}