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            "  Downloading geemap-0.9.4-py2.py3-none-any.whl (493 kB)\n",
            "\u001b[K     |████████████████████████████████| 493 kB 7.2 MB/s \n",
            "\u001b[?25hCollecting jupyterlab>=3\n",
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            "\u001b[K     |████████████████████████████████| 8.6 MB 36.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: earthengine-api>=0.1.230 in /usr/local/lib/python3.7/dist-packages (from geemap) (0.1.290)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from geemap) (1.1.5)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from geemap) (7.1.2)\n",
            "Collecting ipyfilechooser>=0.6.0\n",
            "  Downloading ipyfilechooser-0.6.0-py3-none-any.whl (11 kB)\n",
            "Collecting voila\n",
            "  Downloading voila-0.2.16-py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 62.3 MB/s \n",
            "\u001b[?25hCollecting ipyevents\n",
            "  Downloading ipyevents-2.0.1-py2.py3-none-any.whl (130 kB)\n",
            "\u001b[K     |████████████████████████████████| 130 kB 50.4 MB/s \n",
            "\u001b[?25hCollecting pyshp>=2.1.3\n",
            "  Downloading pyshp-2.1.3.tar.gz (219 kB)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from owslib->geemap) (3.13)\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 17.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from sankee->geemap) (4.4.1)\n",
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            "Collecting jupyter-client\n",
            "  Downloading jupyter_client-6.1.12-py3-none-any.whl (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 78.3 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",
            "Collecting nbconvert\n",
            "  Downloading nbconvert-6.3.0-py3-none-any.whl (556 kB)\n",
            "\u001b[K     |████████████████████████████████| 556 kB 87.2 MB/s \n",
            "\u001b[?25hRequirement 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=57492aa901ebb63f2e442a943362ccfd1820f54a6572b0a9ddccbad406ad19e7\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=ff3e73efd2ae483362a6b8f3a3723865424fe9732826b66b2e84712f15bef295\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=db70abe4bd0e3ce03d1224ee5049fed2a4f6ff207d45ce0c1d48285ce121858c\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=452c3faffb17891e6d90d2afb887d230c859fcba5e86846024fc8311f87cb1d1\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "psB0sOLvrLOb",
        "outputId": "d35cd0de-52b5-432d-a8a0-2f608bcc3e6f"
      },
      "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=Q6XiK8thJK7TUhA09iuGeYqFe9gjfnJGcxxyxpGphDg&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: 4/1AX4XfWgPX8Qzyjb6KAYnizKw3KYkLDTDVBg6vjwR61hSMKnjWeSzbhdoT5s\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "35c95a079b6647d4b04fd143cba330de",
            "3d1f61cd1fa24d5eaed925ecdeca9e36",
            "2edd71761032404a81d36a641b2aa6cc",
            "313fb78a858b45d58ad8df09de0dcb7f",
            "76c6ecd40ef94a88a6e6a9e5f8bb74cb",
            "d382c4d94c3246baaec3dd9674bc05d9",
            "50ea2bc34c2241a7b80b598891f99d59",
            "a4ddb58f887f46808f318a7e38100d1b",
            "6a565003f4dd4d7a9410f5e7bbc791f6",
            "218f52544a0f4f4da125a05928e19b91",
            "1a761aac700043e78e66f76c4ecdd3c9",
            "5c89ff9a56ed4bda8959c15c69c401b3",
            "69dddafdaa484e1891ba9b94917e9cc3",
            "337702eeda3b44f1abfdaf921e0ffbd9",
            "473678a5b4984f2088646b843174fbc5",
            "8aac1439f5d64a42a5c70d51e550416d",
            "3ac13524640548dbad823b737f54540c",
            "b477018564b64638aad675b800d37de2",
            "23742c5f72064356be4c0de1dc733af1",
            "55aea77a04884d4b9c9163cae11e2e6d",
            "9ece5ab00a06477d83bf56a9b5e6cb1e",
            "687cfe1a04f74c70abd0f6b9f391acce",
            "c34da178b3c6477a88cd510927b1a373",
            "569f0db92f8b45a9a2d0775ad55f128f",
            "360d4f3e28c04f4fb9b86f2d2445414f",
            "962b1df8cf8440aaaf7f2e3131dcfb01",
            "4b2da0f0540c4c629fc2374e6d3ad36f",
            "f8d63a0fee044bcf862c085ae70b55a6"
          ]
        },
        "id": "73Dvqhj_rz34",
        "outputId": "9f5bb34b-f82d-455d-ea43-4b303ac0bfa9"
      },
      "source": [
        "Map = geemap.Map()\n",
        "Map"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35c95a079b6647d4b04fd143cba330de",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…"
            ]
          },
          "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": "SN7ex32ergFP"
      },
      "source": [
        "point = ee.Geometry.Point([80.3288, 23.5121])\n",
        "# point = ee.Geometry.Point([-87.7719, 41.8799])\n",
        "\n",
        "image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \\\n",
        "    .filterDate('2016-01-01', '2016-12-31') \\\n",
        "    .sort('CLOUD_COVER') \\\n",
        "    .min() \\\n",
        "    .select('B[1-7]')\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\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJsKtJCHAX0y"
      },
      "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('2016-01-01', '2016-12-31') \\\n",
        "              .select('LC_Type1')\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",
        "map2.centerObject(point, 5)\n",
        "map2.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",
        "\n",
        "#map2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "35c95a079b6647d4b04fd143cba330de",
            "3d1f61cd1fa24d5eaed925ecdeca9e36",
            "2edd71761032404a81d36a641b2aa6cc",
            "313fb78a858b45d58ad8df09de0dcb7f",
            "76c6ecd40ef94a88a6e6a9e5f8bb74cb",
            "d382c4d94c3246baaec3dd9674bc05d9",
            "50ea2bc34c2241a7b80b598891f99d59",
            "a4ddb58f887f46808f318a7e38100d1b",
            "6a565003f4dd4d7a9410f5e7bbc791f6",
            "218f52544a0f4f4da125a05928e19b91",
            "1a761aac700043e78e66f76c4ecdd3c9",
            "5c89ff9a56ed4bda8959c15c69c401b3",
            "69dddafdaa484e1891ba9b94917e9cc3",
            "337702eeda3b44f1abfdaf921e0ffbd9",
            "473678a5b4984f2088646b843174fbc5",
            "4b2da0f0540c4c629fc2374e6d3ad36f",
            "8aac1439f5d64a42a5c70d51e550416d",
            "3ac13524640548dbad823b737f54540c",
            "b477018564b64638aad675b800d37de2",
            "23742c5f72064356be4c0de1dc733af1",
            "55aea77a04884d4b9c9163cae11e2e6d",
            "9ece5ab00a06477d83bf56a9b5e6cb1e",
            "687cfe1a04f74c70abd0f6b9f391acce",
            "c34da178b3c6477a88cd510927b1a373",
            "569f0db92f8b45a9a2d0775ad55f128f",
            "360d4f3e28c04f4fb9b86f2d2445414f",
            "962b1df8cf8440aaaf7f2e3131dcfb01",
            "f8d63a0fee044bcf862c085ae70b55a6"
          ]
        },
        "id": "AgJ2VFhoC7dm",
        "outputId": "ae5d1dc1-cc2f-45c6-b5a9-20bf838988b8"
      },
      "source": [
        "modis = ee.ImageCollection('MODIS/006/MCD12Q1') \\\n",
        "              .filterDate('2016-01-01', '2016-12-31') \\\n",
        "              .select('LC_Type1') \\\n",
        "              .reduce(ee.Reducer.median())\n",
        "\n",
        "points = modis.sample(**{\n",
        "    'region': ee.Geometry.Rectangle([68.0000, 37.0000, 98.0000, 6.0000]),\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": "35c95a079b6647d4b04fd143cba330de",
              "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Zv2TEVrJyaq",
        "outputId": "ca6ff408-f582-416d-c1ec-236f9d5a8435"
      },
      "source": [
        "print(points.size().getInfo())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4930\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ARU8o2PJzQP",
        "outputId": "95bf2de3-45d7-4662-c3fc-7a822193b5ab"
      },
      "source": [
        "print(points.first().getInfo())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [93.94222986465215, 18.244383326707315]}, 'id': '0', 'properties': {'LC_Type1_median': 17}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7R_WuAX0J4ab",
        "outputId": "328bc1f4-8ee0-4003-b019-ac6d435fd194"
      },
      "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': 135, 'B2': 157, 'B3': 99, 'B4': 35, 'B5': 21, 'B6': 33, 'B7': 22, 'LC_Type1_median': 17}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
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        "id": "Q7_C8oSoRKFU",
        "outputId": "d4fca728-6624-427e-9f36-cb5a562cd2d6"
      },
      "source": [
        "# Classify the image with the same bands used for training.\n",
        "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": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35c95a079b6647d4b04fd143cba330de",
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              "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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              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_s6zI0DIn_M"
      },
      "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',   ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      },
      "source": [
        "map2.addLayer(landsatcover, {}, 'Land cover')\n",
        "map2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e0b7808544a43c1955ed9153e82c618",
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            },
            "text/plain": [
              "Map(center=[23.512099999999997, 80.3288], controls=(WidgetControl(options=['position', 'transparent_bg'], widg…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
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    }
  ]
}