2a_run_random_forest_model_2017.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": 44,
   "id": "c1661b94",
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    "# Configure some things up front\n",
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       "  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] 2017-01-01\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-382d7266-b0d0-437b-b882-3425f3f75d8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-382d7266-b0d0-437b-b882-3425f3f75d8c' 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-0ddab11a-9a34-455f-ac30-e7c7e337d10f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ddab11a-9a34-455f-ac30-e7c7e337d10f' 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-c5246fa2-a569-4b95-8a65-50057838ebab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5246fa2-a569-4b95-8a65-50057838ebab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-427c8548-2023-441e-b8ca-0d9858c64376' class='xr-var-data-in' type='checkbox'><label for='data-427c8548-2023-441e-b8ca-0d9858c64376' 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-8ab0945e-bb88-44ad-8463-a1a416836197' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab0945e-bb88-44ad-8463-a1a416836197' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b546617c-9165-45a5-ad71-4836d7ecad01' class='xr-var-data-in' type='checkbox'><label for='data-b546617c-9165-45a5-ad71-4836d7ecad01' 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-51f11556-b811-41df-8b60-326c2dedc1d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51f11556-b811-41df-8b60-326c2dedc1d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05b9b4e0-c3ad-429c-925b-c9eebe58e756' class='xr-var-data-in' type='checkbox'><label for='data-05b9b4e0-c3ad-429c-925b-c9eebe58e756' 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'>2017-01-01</div><input id='attrs-bd65fdec-88db-4870-9a2c-5390539ff4c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd65fdec-88db-4870-9a2c-5390539ff4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9db9128b-bd8f-4109-933c-5f29f135cecb' class='xr-var-data-in' type='checkbox'><label for='data-9db9128b-bd8f-4109-933c-5f29f135cecb' 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;2017-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-095db835-2470-4a96-9e95-5e2c5af035fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-095db835-2470-4a96-9e95-5e2c5af035fc' 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-f2d1dd9d-9113-4e24-8bbe-47d38e2dcd49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2d1dd9d-9113-4e24-8bbe-47d38e2dcd49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9373d36d-92db-4d56-a721-51304e20ca76' class='xr-var-data-in' type='checkbox'><label for='data-9373d36d-92db-4d56-a721-51304e20ca76' 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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       "\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
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       "  <!-- Text -->\n",
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       "  <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-f041e256-acf9-4019-b749-774812b8aa44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f041e256-acf9-4019-b749-774812b8aa44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53cb6786-32fd-42c9-8757-a4d7782ea897' class='xr-var-data-in' type='checkbox'><label for='data-53cb6786-32fd-42c9-8757-a4d7782ea897' 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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       "\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",
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       "\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-116d7bc5-b4e4-4419-a488-c77a77502140' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-116d7bc5-b4e4-4419-a488-c77a77502140' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b461739a-0dcb-4ab7-890f-70cd6aca52eb' class='xr-var-data-in' type='checkbox'><label for='data-b461739a-0dcb-4ab7-890f-70cd6aca52eb' 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>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-3294f6f3-57ff-42c6-bacc-8f52ee8b49da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3294f6f3-57ff-42c6-bacc-8f52ee8b49da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c78eff1-85b7-48ae-979c-73a52946055a' class='xr-var-data-in' type='checkbox'><label for='data-2c78eff1-85b7-48ae-979c-73a52946055a' 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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       "\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-1785f0ac-4436-4d88-89ba-651aa8461712' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1785f0ac-4436-4d88-89ba-651aa8461712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0ee1b3a-a6bd-4abb-bb27-115a6e32423b' class='xr-var-data-in' type='checkbox'><label for='data-a0ee1b3a-a6bd-4abb-bb27-115a6e32423b' 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>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-73f4dd98-ec56-4616-bd7c-99e968dbd039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73f4dd98-ec56-4616-bd7c-99e968dbd039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-736788a8-79cc-4962-9e83-edfb2c42e55a' class='xr-var-data-in' type='checkbox'><label for='data-736788a8-79cc-4962-9e83-edfb2c42e55a' 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-e1fbb637-8a95-481d-9c5f-a780f939762f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1fbb637-8a95-481d-9c5f-a780f939762f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83e31a9-7cb3-495d-9e81-a17c74697c82' class='xr-var-data-in' type='checkbox'><label for='data-c83e31a9-7cb3-495d-9e81-a17c74697c82' 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-5f764f4e-036b-4851-99ec-b8d8b5e0fb9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f764f4e-036b-4851-99ec-b8d8b5e0fb9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c3e8482-f6f6-46b5-831d-1e532af24749' class='xr-var-data-in' type='checkbox'><label for='data-1c3e8482-f6f6-46b5-831d-1e532af24749' 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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       "\n",
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       "  <!-- Text -->\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-8e202e5b-109e-4722-803e-4eb3397ff82d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e202e5b-109e-4722-803e-4eb3397ff82d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-341cd4ee-2e93-4854-830e-40cd016160ea' class='xr-var-data-in' type='checkbox'><label for='data-341cd4ee-2e93-4854-830e-40cd016160ea' 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>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-c8c81b01-0fbc-4b45-b782-16b5e5392293' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8c81b01-0fbc-4b45-b782-16b5e5392293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55092407-7a3c-4542-820c-f917f0ed240e' class='xr-var-data-in' type='checkbox'><label for='data-55092407-7a3c-4542-820c-f917f0ed240e' 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",
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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",
       "                        <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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       "                        <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> 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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       "                    <tr>\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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       "                        <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",
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       "                    <tr>\n",
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       "                        <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> 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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       "                        <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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       "                        <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 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-954b1107-4c26-41b3-a131-10115ef006cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-954b1107-4c26-41b3-a131-10115ef006cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4d905e6-bb50-4084-86b9-d9315ceadf1c' class='xr-var-data-in' type='checkbox'><label for='data-b4d905e6-bb50-4084-86b9-d9315ceadf1c' 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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       "                    <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",
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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-4acd5318-b634-4cc8-9390-ec6fdf435f3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4acd5318-b634-4cc8-9390-ec6fdf435f3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7615b157-d520-48f5-84ff-97083665e18d' class='xr-var-data-in' type='checkbox'><label for='data-7615b157-d520-48f5-84ff-97083665e18d' 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-0e0b8677-1cb1-4199-a224-4def60468588' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e0b8677-1cb1-4199-a224-4def60468588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24c869c7-99fd-48a3-a0c7-c7355d6014d1' class='xr-var-data-in' type='checkbox'><label for='data-24c869c7-99fd-48a3-a0c7-c7355d6014d1' 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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       "                    <tr>\n",
       "                        <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> 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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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-57eddeea-093c-4d05-be01-ad72b6b79f4b' class='xr-section-summary-in' type='checkbox'  ><label for='section-57eddeea-093c-4d05-be01-ad72b6b79f4b' 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-ee096fae-84cc-41f4-a677-9d6c8bcac5a9' class='xr-index-data-in' type='checkbox'/><label for='index-ee096fae-84cc-41f4-a677-9d6c8bcac5a9' 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-66da91ba-ec98-4e4e-af18-68f4e3bb6427' class='xr-index-data-in' type='checkbox'/><label for='index-66da91ba-ec98-4e4e-af18-68f4e3bb6427' 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-e6a79a44-5f39-4892-b297-e86d224ca99e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e6a79a44-5f39-4892-b297-e86d224ca99e' 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] 2017-01-01\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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = load_data(bbox, datetime=datetime, chunks=chunks, resolution=10)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "af9762ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_model = joblib.load(\"test_model.dump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "id": "cb18ecb5-9a5c-4337-b147-c08d59e4fd87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filled = loaded_data.fillna(-9999.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "id": "c2c96a26-db0b-421d-ad70-7438d8a4ca81",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  display: contents;\n",
       "}\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 3 3 3 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 4\n",
       "    prob         (y, x) float32 55.45 69.05 78.05 83.02 ... 71.26 39.34 41.07\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-ba8a4c05-a539-4f36-b840-f13c3169749c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba8a4c05-a539-4f36-b840-f13c3169749c' 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-3f696c5d-8b5e-4769-8ef3-0accab06eaac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f696c5d-8b5e-4769-8ef3-0accab06eaac' 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-2bbd294d-2fca-4929-99e7-8235bf7117a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bbd294d-2fca-4929-99e7-8235bf7117a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80cbf29a-3625-4201-9428-d8f71a2b6edc' class='xr-var-data-in' type='checkbox'><label for='data-80cbf29a-3625-4201-9428-d8f71a2b6edc' 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-57f66aa6-c02a-4e2b-8618-5a97940e3c5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57f66aa6-c02a-4e2b-8618-5a97940e3c5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be6b02f8-2cf0-4e0b-abca-f7a4a8409004' class='xr-var-data-in' type='checkbox'><label for='data-be6b02f8-2cf0-4e0b-abca-f7a4a8409004' 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-9c849adf-cfce-4416-af15-f686b24c6d38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c849adf-cfce-4416-af15-f686b24c6d38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f9c642-da30-4d0e-82a2-decd3ffd4089' class='xr-var-data-in' type='checkbox'><label for='data-87f9c642-da30-4d0e-82a2-decd3ffd4089' 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-47c3e4c9-92c1-4716-a956-3d8be698837f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47c3e4c9-92c1-4716-a956-3d8be698837f' 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'>3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4</div><input id='attrs-ac863441-ce40-4877-85f4-37720a7ceb8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac863441-ce40-4877-85f4-37720a7ceb8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31f1044f-f003-481d-8872-183d86d38be5' class='xr-var-data-in' type='checkbox'><label for='data-31f1044f-f003-481d-8872-183d86d38be5' 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([[3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 2, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 4]], 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'>55.45 69.05 78.05 ... 39.34 41.07</div><input id='attrs-c3df681a-9b97-45eb-8666-d8c9ec9ba9a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3df681a-9b97-45eb-8666-d8c9ec9ba9a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c90616ac-9e3b-40d4-8794-9271799f62bf' class='xr-var-data-in' type='checkbox'><label for='data-c90616ac-9e3b-40d4-8794-9271799f62bf' 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([[55.453598, 69.045265, 78.047066, ..., 48.738003, 50.63763 ,\n",
       "        66.88132 ],\n",
       "       [51.864098, 63.0237  , 66.01166 , ..., 52.855953, 51.522995,\n",
       "        70.14338 ],\n",
       "       [58.87266 , 73.62939 , 59.766727, ..., 37.39258 , 35.005585,\n",
       "        72.57198 ],\n",
       "       ...,\n",
       "       [49.91194 , 44.24936 , 38.591663, ..., 78.15298 , 49.65203 ,\n",
       "        47.850845],\n",
       "       [53.06502 , 41.59724 , 42.36086 , ..., 74.070595, 45.562065,\n",
       "        46.037918],\n",
       "       [56.751396, 44.71969 , 42.678944, ..., 71.25749 , 39.344975,\n",
       "        41.06662 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22ea7de8-71ca-4114-b731-95ffbe2bb8e3' class='xr-section-summary-in' type='checkbox'  ><label for='section-22ea7de8-71ca-4114-b731-95ffbe2bb8e3' 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-37a736be-9ea3-4c44-aade-ab9dd33633d4' class='xr-index-data-in' type='checkbox'/><label for='index-37a736be-9ea3-4c44-aade-ab9dd33633d4' 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-eb2fcd83-6e65-48d1-a8c9-46f3410afefa' class='xr-index-data-in' type='checkbox'/><label for='index-eb2fcd83-6e65-48d1-a8c9-46f3410afefa' 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-2fac946f-79f9-4ebf-9380-3fd4475060b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2fac946f-79f9-4ebf-9380-3fd4475060b2' 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 3 3 3 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 4\n",
       "    prob         (y, x) float32 55.45 69.05 78.05 83.02 ... 71.26 39.34 41.07\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "949ce84c-6a9a-4918-aaf2-8438b6e87feb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f00c3ff1090>"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "id": "c3d25ca4-dc50-4b2f-8ddc-bfc1274393b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('lulc_2017.tif')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write GeoTIFF\n",
    "cleaned_predictions.lulc.odc.write_cog(\"lulc_2017.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "24a48d19-b363-4e1a-8edd-b39f34146a80",
   "metadata": {},
   "outputs": [
    {
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       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\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 3 3 3 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 4\n",
       "    prob         (y, x) float32 55.45 69.05 78.05 83.02 ... 71.26 39.34 41.07\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-4d4ed603-d36b-46d7-9b3f-6e4e05b5790d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d4ed603-d36b-46d7-9b3f-6e4e05b5790d' 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-cdc1fb13-9d11-42fc-9db5-4d3ecdf0646c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdc1fb13-9d11-42fc-9db5-4d3ecdf0646c' 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-dff2e8b5-0581-4f49-ac39-a976d4a30e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff2e8b5-0581-4f49-ac39-a976d4a30e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637e286c-072a-4fb1-a816-3a10062fd356' class='xr-var-data-in' type='checkbox'><label for='data-637e286c-072a-4fb1-a816-3a10062fd356' 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-5dd3ef36-a2e6-4e20-a526-3b3027ace6e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dd3ef36-a2e6-4e20-a526-3b3027ace6e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a39051c-f518-48d5-8fed-aca4e22c7e84' class='xr-var-data-in' type='checkbox'><label for='data-0a39051c-f518-48d5-8fed-aca4e22c7e84' 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-4fd66df9-6ff9-4e60-bab6-48251049d18d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fd66df9-6ff9-4e60-bab6-48251049d18d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96270626-e27c-47f5-8772-b774fdd6d69c' class='xr-var-data-in' type='checkbox'><label for='data-96270626-e27c-47f5-8772-b774fdd6d69c' 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-5420f0d4-b68b-4aee-9453-b45e1bac7553' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5420f0d4-b68b-4aee-9453-b45e1bac7553' 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'>3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4</div><input id='attrs-99d5ea67-fa26-411b-a5d2-0929c6f5960b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99d5ea67-fa26-411b-a5d2-0929c6f5960b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-084ed68f-d8d1-4650-b35e-df98b81e3a63' class='xr-var-data-in' type='checkbox'><label for='data-084ed68f-d8d1-4650-b35e-df98b81e3a63' 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([[3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 2, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 4]], 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'>55.45 69.05 78.05 ... 39.34 41.07</div><input id='attrs-1a757848-b258-4104-9d99-a3cce1d832e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a757848-b258-4104-9d99-a3cce1d832e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51c2a438-9517-4a14-829c-b2f84c8b2878' class='xr-var-data-in' type='checkbox'><label for='data-51c2a438-9517-4a14-829c-b2f84c8b2878' 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([[55.453598, 69.045265, 78.047066, ..., 48.738003, 50.63763 ,\n",
       "        66.88132 ],\n",
       "       [51.864098, 63.0237  , 66.01166 , ..., 52.855953, 51.522995,\n",
       "        70.14338 ],\n",
       "       [58.87266 , 73.62939 , 59.766727, ..., 37.39258 , 35.005585,\n",
       "        72.57198 ],\n",
       "       ...,\n",
       "       [49.91194 , 44.24936 , 38.591663, ..., 78.15298 , 49.65203 ,\n",
       "        47.850845],\n",
       "       [53.06502 , 41.59724 , 42.36086 , ..., 74.070595, 45.562065,\n",
       "        46.037918],\n",
       "       [56.751396, 44.71969 , 42.678944, ..., 71.25749 , 39.344975,\n",
       "        41.06662 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef4de6b9-c988-4695-bf1e-f8d13ebb146b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef4de6b9-c988-4695-bf1e-f8d13ebb146b' 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-823cdf95-4b9e-4d19-878e-61e7b53e42b3' class='xr-index-data-in' type='checkbox'/><label for='index-823cdf95-4b9e-4d19-878e-61e7b53e42b3' 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-eb7d33de-1ae7-459b-bf73-cb2eaf4b76cc' class='xr-index-data-in' type='checkbox'/><label for='index-eb7d33de-1ae7-459b-bf73-cb2eaf4b76cc' 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-79b5fa57-5ebd-44fc-ad3b-416bd1c9110b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79b5fa57-5ebd-44fc-ad3b-416bd1c9110b' 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 3 3 3 3 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 4\n",
       "    prob         (y, x) float32 55.45 69.05 78.05 83.02 ... 71.26 39.34 41.07\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4931f399-7510-4436-8873-d834b908e656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Test_change_detection/bareland_2017.tif')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarry_2017 = cleaned_predictions.lulc.isin([8])\n",
    "quarry_2017 = quarry_2017.astype(np.int32)\n",
    "quarry_2017.odc.write_cog(\"Test_change_detection/quarry_2017.tif\", overwrite=True)\n",
    "bareland_2017 = cleaned_predictions.lulc.isin([1])\n",
    "bareland_2017 = bareland_2017.astype(np.int32)\n",
    "bareland_2017.odc.write_cog(\"Test_change_detection/bareland_2017.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e997d9fc-5ad4-4387-80d0-25623282ae52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "quarry_2017 = xr.open_dataset(\"Test_change_detection/quarry_2017.tif\")\n",
    "bareland_2017 = xr.open_dataset(\"Test_change_detection/bareland_2017.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e4be63c6-ff24-4ed3-8217-827b91ab2b3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f00c3dbf890>"
      ]
     },
     "execution_count": 56,
     "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_2017.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a04ee15c-80ce-4d24-8621-6c7493e4a18a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f00c3d95dd0>"
      ]
     },
     "execution_count": 58,
     "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_2017.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2b0c0f32-1c97-417a-88fd-ccbd3ef5ca19",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished\n"
     ]
    }
   ],
   "source": [
    "print(\"Finished\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bbacb3-c17b-43f2-833c-064c554eed9a",
   "metadata": {},
   "source": [
    "End of notebook"
   ]
  }
 ],
 "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",
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