2c_run_random_forest_model_2019.ipynb

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   "source": [
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import odc.geo\n",
    "from odc.stac import load\n",
    "from pystac_client import Client\n",
    "from shapely import geometry\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import datacube\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.abspath('../Tools'))\n",
    "\n",
    "import rasterio\n",
    "import matplotlib\n",
    "import pickle\n",
    "import joblib\n",
    "from utils import load_data, predict_xr"
   ]
  },
  {
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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
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   "source": [
    "## Find and load data\n",
    "\n",
    "Load data and set up your array to use for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c1661b94",
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    "# Configure some things up front\n",
    "chunks = dict(x=2048, y=2048)\n",
    "datetime = \"2019\"\n",
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       "  display: inline-block !important;\n",
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       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (y: 1007, x: 1646)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "    spatial_ref  int32 3832\n",
       "    time         datetime64[ns] 2019-01-01\n",
       "Data variables: (12/24)\n",
       "    B02          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B03          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B04          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B05          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B06          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B07          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    ...           ...\n",
       "    ndmi         (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    ndbi         (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    elevation    (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vv      (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vh      (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vv_vh   (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-935c939e-9001-4e3a-b355-cd8a643f5481' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-935c939e-9001-4e3a-b355-cd8a643f5481' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1007</li><li><span class='xr-has-index'>x</span>: 1646</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7e73172-f031-4c45-b94b-abccb3a20e8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7e73172-f031-4c45-b94b-abccb3a20e8d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.989e+06 ... -1.999e+06</div><input id='attrs-42455fa6-2980-4efe-8b46-d6c5483663fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42455fa6-2980-4efe-8b46-d6c5483663fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19bcf651-399c-439c-abf0-0afbe0a28cfc' class='xr-var-data-in' type='checkbox'><label for='data-19bcf651-399c-439c-abf0-0afbe0a28cfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-f219e44a-b6e4-4465-9c3a-f32ac943d889' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f219e44a-b6e4-4465-9c3a-f32ac943d889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68a0377b-5cda-4215-9de9-b3845747cec7' class='xr-var-data-in' type='checkbox'><label for='data-68a0377b-5cda-4215-9de9-b3845747cec7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-515c00c3-bbe1-4863-8e95-3edd50bf7c32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515c00c3-bbe1-4863-8e95-3edd50bf7c32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09baaf98-df98-4836-bd34-008c67258ed6' class='xr-var-data-in' type='checkbox'><label for='data-09baaf98-df98-4836-bd34-008c67258ed6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / PDC Mercator</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd><dt><span>standard_parallel :</span></dt><dd>0.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>150.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_projection_origin :</span></dt><dd>1.0</dd><dt><span>GeoTransform :</span></dt><dd>3054850.0 10.0 0.0 -1989040.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-01</div><input id='attrs-840788f9-4d13-4384-b170-33b8547487c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-840788f9-4d13-4384-b170-33b8547487c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1520290-82e1-4082-8488-b2c742093541' class='xr-var-data-in' type='checkbox'><label for='data-c1520290-82e1-4082-8488-b2c742093541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2019-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b29eea71-61ed-4575-8cf8-03edd566aaf2' class='xr-section-summary-in' type='checkbox'  ><label for='section-b29eea71-61ed-4575-8cf8-03edd566aaf2' class='xr-section-summary' >Data variables: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>B02</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-2229e048-5c58-46db-8bcd-45451251d8e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2229e048-5c58-46db-8bcd-45451251d8e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2360ae47-eece-4c25-8a56-065df7e7e26d' class='xr-var-data-in' type='checkbox'><label for='data-2360ae47-eece-4c25-8a56-065df7e7e26d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B03</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-2e9e8c78-a9a4-48ab-be21-b9a4d8a4b9a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e9e8c78-a9a4-48ab-be21-b9a4d8a4b9a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23c9a3bf-c116-4d9d-b57f-0f97c4a1774b' class='xr-var-data-in' type='checkbox'><label for='data-23c9a3bf-c116-4d9d-b57f-0f97c4a1774b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B04</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-a4800c73-b503-4358-a381-d84dd63e058f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4800c73-b503-4358-a381-d84dd63e058f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e672223-da8c-483c-893c-885fb371aa6e' class='xr-var-data-in' type='checkbox'><label for='data-6e672223-da8c-483c-893c-885fb371aa6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B05</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-82190556-215e-4814-9be3-26c279c41fe8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82190556-215e-4814-9be3-26c279c41fe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0cca2d0-091f-4577-b512-dad7aeea0a33' class='xr-var-data-in' type='checkbox'><label for='data-a0cca2d0-091f-4577-b512-dad7aeea0a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B06</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-b5c184e5-1b42-45cd-90c2-55f78cd8b0ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5c184e5-1b42-45cd-90c2-55f78cd8b0ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e12f13e-bfef-49d1-ac39-75b6f4f4d44c' class='xr-var-data-in' type='checkbox'><label for='data-3e12f13e-bfef-49d1-ac39-75b6f4f4d44c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B07</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-1b511abe-8966-4dc2-9efe-64997a7e37b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b511abe-8966-4dc2-9efe-64997a7e37b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a194400-2d11-4467-a2ed-8ba21549c53e' class='xr-var-data-in' type='checkbox'><label for='data-4a194400-2d11-4467-a2ed-8ba21549c53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B08</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-82434b16-42b9-4bd5-95a6-9b785a89e6fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82434b16-42b9-4bd5-95a6-9b785a89e6fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07963673-be87-4628-84b7-d833672d41c6' class='xr-var-data-in' type='checkbox'><label for='data-07963673-be87-4628-84b7-d833672d41c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B8A</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-d7b66979-ee74-415d-ac07-79d70f664e0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7b66979-ee74-415d-ac07-79d70f664e0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d656fb72-4bac-4bc5-b541-8f155216f0d9' class='xr-var-data-in' type='checkbox'><label for='data-d656fb72-4bac-4bc5-b541-8f155216f0d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B11</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-35c83bec-d464-4677-b0cd-11bd0f6042cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35c83bec-d464-4677-b0cd-11bd0f6042cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-005fb80a-b452-4ae1-80b6-e491f79d96b2' class='xr-var-data-in' type='checkbox'><label for='data-005fb80a-b452-4ae1-80b6-e491f79d96b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B12</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-bb7c4318-ad61-4eeb-bc2a-076c1e9b359b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb7c4318-ad61-4eeb-bc2a-076c1e9b359b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a366225-8f25-4688-95be-2d7608ca63e6' class='xr-var-data-in' type='checkbox'><label for='data-6a366225-8f25-4688-95be-2d7608ca63e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 6.32 MiB </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 13 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndmi</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-b35829b6-02e3-4829-b521-8fc27b740fdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b35829b6-02e3-4829-b521-8fc27b740fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df5782e4-e466-459b-ba71-033ba321b9e1' class='xr-var-data-in' type='checkbox'><label for='data-df5782e4-e466-459b-ba71-033ba321b9e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndbi</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-7a01962b-5e01-46f0-b941-5c341fc82869' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a01962b-5e01-46f0-b941-5c341fc82869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6aeed9d1-27a9-49e4-bdc9-0d8fcd04d12d' class='xr-var-data-in' type='checkbox'><label for='data-6aeed9d1-27a9-49e4-bdc9-0d8fcd04d12d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-92fe21c4-8fc1-4edf-871c-3edb76521326' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92fe21c4-8fc1-4edf-871c-3edb76521326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306a9527-a5b2-4cdc-abf5-22cdeb4e166e' class='xr-var-data-in' type='checkbox'><label for='data-306a9527-a5b2-4cdc-abf5-22cdeb4e166e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a26c2664-4164-4806-829d-949ed2b8709b' class='xr-section-summary-in' type='checkbox'  ><label for='section-a26c2664-4164-4806-829d-949ed2b8709b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc7162c3-bb4b-4cf4-8161-d6e87eeeec12' class='xr-index-data-in' type='checkbox'/><label for='index-bc7162c3-bb4b-4cf4-8161-d6e87eeeec12' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-20a57bcf-9e2a-4ee5-b221-a818b0a058c1' class='xr-index-data-in' type='checkbox'/><label for='index-20a57bcf-9e2a-4ee5-b221-a818b0a058c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b966f460-4b8a-48ba-b096-1b1e8d8289e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b966f460-4b8a-48ba-b096-1b1e8d8289e9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (y: 1007, x: 1646)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "    spatial_ref  int32 3832\n",
       "    time         datetime64[ns] 2019-01-01\n",
       "Data variables: (12/24)\n",
       "    B02          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B03          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B04          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B05          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B06          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B07          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    ...           ...\n",
       "    ndmi         (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    ndbi         (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    elevation    (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vv      (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vh      (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vv_vh   (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = load_data(bbox, datetime=datetime, chunks=chunks, resolution=10)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "af9762ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_model = joblib.load(\"test_model.dump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2f4a57c1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_data = merged.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b942be-dbbe-44c2-8291-d55a5e6d8249",
   "metadata": {},
   "source": [
    "## Run ML classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cb18ecb5-9a5c-4337-b147-c08d59e4fd87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filled = loaded_data.fillna(-9999.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07c9cfa5-ff5a-4fc9-b56d-2cd8c8a6e553",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicting...\n",
      "   probabilities...\n"
     ]
    }
   ],
   "source": [
    "# This runs the actual prediction\n",
    "predicted = predict_xr(loaded_model, filled, proba=True)\n",
    "\n",
    "# Convert to int\n",
    "cleaned_predictions = predicted.copy(deep=True)\n",
    "cleaned_predictions.predictions.data = predicted.predictions.data.astype(np.int8)\n",
    "cleaned_predictions.probabilities.data = predicted.probabilities.data.astype(np.float32)\n",
    "\n",
    "cleaned_predictions = cleaned_predictions.rename({\"predictions\": \"lulc\", \"probabilities\": \"prob\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2c96a26-db0b-421d-ad70-7438d8a4ca81",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 1 1 1 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 36.83 39.6 32.55 37.8 ... 47.23 82.33 39.4 70.99\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af7bdf5c-e7b0-467e-88f4-f0e41756d327' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af7bdf5c-e7b0-467e-88f4-f0e41756d327' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1646</li><li><span class='xr-has-index'>y</span>: 1007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57992bf8-5d6f-46ef-83cc-528a4dcf8e00' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57992bf8-5d6f-46ef-83cc-528a4dcf8e00' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-86068168-b5b9-4c25-86b6-f435e1b65b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86068168-b5b9-4c25-86b6-f435e1b65b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1622ac84-8fbd-4992-8f57-920937d6de31' class='xr-var-data-in' type='checkbox'><label for='data-1622ac84-8fbd-4992-8f57-920937d6de31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.989e+06 ... -1.999e+06</div><input id='attrs-12c05dd2-6c11-4304-9865-fc741e64af8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12c05dd2-6c11-4304-9865-fc741e64af8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ad2d010-d564-4ddc-9c59-0c2be0b5e0f1' class='xr-var-data-in' type='checkbox'><label for='data-9ad2d010-d564-4ddc-9c59-0c2be0b5e0f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-3df02cfd-f408-4fd3-a8cb-2b910c84736b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3df02cfd-f408-4fd3-a8cb-2b910c84736b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9061391a-67bf-4e36-9335-dbf7a7967173' class='xr-var-data-in' type='checkbox'><label for='data-9061391a-67bf-4e36-9335-dbf7a7967173' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / PDC Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,150],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3832&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ac154e8-6b47-4617-8d8c-073179f2d01c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ac154e8-6b47-4617-8d8c-073179f2d01c' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lulc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>1 1 1 3 3 3 3 3 ... 4 4 4 4 4 4 2 2</div><input id='attrs-a1ee78e5-149c-4eef-a2cb-88d8397adac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1ee78e5-149c-4eef-a2cb-88d8397adac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2bf8c61-c7ab-4b62-bf32-671020de1085' class='xr-var-data-in' type='checkbox'><label for='data-f2bf8c61-c7ab-4b62-bf32-671020de1085' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 2, 2, 2],\n",
       "       [8, 1, 3, ..., 2, 2, 4],\n",
       "       [8, 3, 3, ..., 2, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 2],\n",
       "       [3, 3, 3, ..., 4, 2, 2]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prob</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>36.83 39.6 32.55 ... 39.4 70.99</div><input id='attrs-fe73862e-6440-4e15-aa3a-64b3a31c9be3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe73862e-6440-4e15-aa3a-64b3a31c9be3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57aa7663-c4b1-4b9c-aac4-cc91390b9127' class='xr-var-data-in' type='checkbox'><label for='data-57aa7663-c4b1-4b9c-aac4-cc91390b9127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[36.83054 , 39.60359 , 32.550613, ..., 76.19354 , 34.55627 ,\n",
       "        35.132683],\n",
       "       [38.046104, 31.648743, 44.131805, ..., 81.687294, 74.69955 ,\n",
       "        47.983643],\n",
       "       [41.35349 , 38.41152 , 55.280716, ..., 78.29831 , 80.45946 ,\n",
       "        50.08714 ],\n",
       "       ...,\n",
       "       [46.366585, 49.180595, 40.87499 , ..., 77.992256, 47.82406 ,\n",
       "        47.364017],\n",
       "       [49.951595, 43.784805, 50.973698, ..., 77.59415 , 35.638023,\n",
       "        56.696293],\n",
       "       [54.849094, 51.958576, 51.48381 , ..., 82.33375 , 39.40055 ,\n",
       "        70.991486]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2499b6ec-4f95-45d9-81a0-2a14d2f1fa97' class='xr-section-summary-in' type='checkbox'  ><label for='section-2499b6ec-4f95-45d9-81a0-2a14d2f1fa97' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85bbe8e5-1508-4ce9-860f-e3d44f4464e8' class='xr-index-data-in' type='checkbox'/><label for='index-85bbe8e5-1508-4ce9-860f-e3d44f4464e8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3fd6e98f-6ba9-4031-959b-699d7385ef80' class='xr-index-data-in' type='checkbox'/><label for='index-3fd6e98f-6ba9-4031-959b-699d7385ef80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47261d23-e635-4140-8a40-ec51df0926cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47261d23-e635-4140-8a40-ec51df0926cc' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 1 1 1 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 36.83 39.6 32.55 37.8 ... 47.23 82.33 39.4 70.99\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "949ce84c-6a9a-4918-aaf2-8438b6e87feb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9fcdd25d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1333.33x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "\n",
    "classes = [\n",
    "    [1, \"bare_land\", \"#968640\"],\n",
    "    [2, \"forest\", \"#064a00\"],\n",
    "    [3, \"crops\", \"#ffce33\"],\n",
    "    [4, \"grassland\", \"#d7ffa0\"],\n",
    "    [5, \"settlements\", \"#b3b2ae\"],\n",
    "    [6, \"mangroves\", \"#07b28d\"],\n",
    "    [7, \"water\", \"#71a8ff\"],\n",
    "    [8, \"quarry\", \"#b03a2e\"]\n",
    "]\n",
    "\n",
    "values_list = [c[0] for c in classes]\n",
    "color_list = [c[2] for c in classes]\n",
    "\n",
    "# Build a listed colormap.\n",
    "c_map = colors.ListedColormap(color_list)\n",
    "bounds = values_list + [9]\n",
    "norm = colors.BoundaryNorm(bounds, c_map.N)\n",
    "\n",
    "cleaned_predictions.lulc.plot.imshow(cmap=c_map, norm=norm, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3d25ca4-dc50-4b2f-8ddc-bfc1274393b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('lulc_2019.tif')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write GeoTIFF\n",
    "cleaned_predictions.lulc.odc.write_cog(\"lulc_2019.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "24a48d19-b363-4e1a-8edd-b39f34146a80",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 1 1 1 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 36.83 39.6 32.55 37.8 ... 47.23 82.33 39.4 70.99\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4a92acb5-a6b3-48fc-9f87-0d587623cccf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a92acb5-a6b3-48fc-9f87-0d587623cccf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1646</li><li><span class='xr-has-index'>y</span>: 1007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a7cec32e-19d4-4982-a0f5-35f6fb31008f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7cec32e-19d4-4982-a0f5-35f6fb31008f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-5f2d365d-e57c-4df8-8796-fcd868f158bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f2d365d-e57c-4df8-8796-fcd868f158bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-194d4e3f-3879-4e79-9f0b-508eebc9f258' class='xr-var-data-in' type='checkbox'><label for='data-194d4e3f-3879-4e79-9f0b-508eebc9f258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.989e+06 ... -1.999e+06</div><input id='attrs-9a46834f-aabd-4ef2-81a3-e911d2f25ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a46834f-aabd-4ef2-81a3-e911d2f25ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27026864-282d-445f-96f9-1efb35651ba9' class='xr-var-data-in' type='checkbox'><label for='data-27026864-282d-445f-96f9-1efb35651ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-e1bf218b-03b8-4072-880b-09b89b8a10e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1bf218b-03b8-4072-880b-09b89b8a10e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d2b02f8-07c0-4d0b-842a-4a0f21f0cee4' class='xr-var-data-in' type='checkbox'><label for='data-2d2b02f8-07c0-4d0b-842a-4a0f21f0cee4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / PDC Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,150],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3832&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7a76579-c560-4a28-a053-58f08d75ffa3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7a76579-c560-4a28-a053-58f08d75ffa3' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lulc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>1 1 1 3 3 3 3 3 ... 4 4 4 4 4 4 2 2</div><input id='attrs-9890b188-c8b4-4d0f-894a-d087ec580ef6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9890b188-c8b4-4d0f-894a-d087ec580ef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4cb12a5-b85c-4586-a85c-2df5e80827b1' class='xr-var-data-in' type='checkbox'><label for='data-a4cb12a5-b85c-4586-a85c-2df5e80827b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 2, 2, 2],\n",
       "       [8, 1, 3, ..., 2, 2, 4],\n",
       "       [8, 3, 3, ..., 2, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 2],\n",
       "       [3, 3, 3, ..., 4, 2, 2]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prob</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>36.83 39.6 32.55 ... 39.4 70.99</div><input id='attrs-15c0fd0a-f318-45fc-8cec-3d30dbfeb45b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15c0fd0a-f318-45fc-8cec-3d30dbfeb45b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de8de095-262f-406d-afdd-9ee77a77e752' class='xr-var-data-in' type='checkbox'><label for='data-de8de095-262f-406d-afdd-9ee77a77e752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[36.83054 , 39.60359 , 32.550613, ..., 76.19354 , 34.55627 ,\n",
       "        35.132683],\n",
       "       [38.046104, 31.648743, 44.131805, ..., 81.687294, 74.69955 ,\n",
       "        47.983643],\n",
       "       [41.35349 , 38.41152 , 55.280716, ..., 78.29831 , 80.45946 ,\n",
       "        50.08714 ],\n",
       "       ...,\n",
       "       [46.366585, 49.180595, 40.87499 , ..., 77.992256, 47.82406 ,\n",
       "        47.364017],\n",
       "       [49.951595, 43.784805, 50.973698, ..., 77.59415 , 35.638023,\n",
       "        56.696293],\n",
       "       [54.849094, 51.958576, 51.48381 , ..., 82.33375 , 39.40055 ,\n",
       "        70.991486]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-929856fb-de3f-476f-a3e3-e68b3ba992cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-929856fb-de3f-476f-a3e3-e68b3ba992cd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-15f943d1-fc96-4351-8157-522eea563d45' class='xr-index-data-in' type='checkbox'/><label for='index-15f943d1-fc96-4351-8157-522eea563d45' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b125d73-78ed-44b0-a03b-cb70cd120554' class='xr-index-data-in' type='checkbox'/><label for='index-4b125d73-78ed-44b0-a03b-cb70cd120554' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c4d26f9-217c-4c8e-a333-dc18eb853497' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c4d26f9-217c-4c8e-a333-dc18eb853497' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 1 1 1 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 36.83 39.6 32.55 37.8 ... 47.23 82.33 39.4 70.99\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4931f399-7510-4436-8873-d834b908e656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Test_change_detection/bareland_2019.tif')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarry_2019 = cleaned_predictions.lulc.isin([8])\n",
    "quarry_2019 = quarry_2019.astype(np.int32)\n",
    "quarry_2019.odc.write_cog(\"Test_change_detection/quarry_2019.tif\", overwrite=True)\n",
    "bareland_2019 = cleaned_predictions.lulc.isin([1])\n",
    "bareland_2019 = bareland_2019.astype(np.int32)\n",
    "bareland_2019.odc.write_cog(\"Test_change_detection/bareland_2019.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e997d9fc-5ad4-4387-80d0-25623282ae52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "quarry_2019 = xr.open_dataset(\"Test_change_detection/quarry_2019.tif\")\n",
    "bareland_2019 = xr.open_dataset(\"Test_change_detection/bareland_2019.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e4be63c6-ff24-4ed3-8217-827b91ab2b3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7ff9e1d7f210>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666.667x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quarry_2019.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a04ee15c-80ce-4d24-8621-6c7493e4a18a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7ff9e1dcba50>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666.667x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bareland_2019.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}