0_Generate_Training_Points.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bb016a6-d175-4017-84f4-92290942069c",
   "metadata": {},
   "source": [
    "# Generate Training Points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece9bfe1-7aec-4eb2-b687-6b2a3e3e22a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Background\n",
    "\n",
    "**Training data** is the most important part of any supervised machine learning workflow. The quality of the training data has a greater impact on the classification than the algorithm used. Large and accurate training data sets are preferable: increasing the training sample size results in increased classification accuracy ([Maxell et al 2018](https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1433343)).  A review of training data methods in the context of Earth Observation is available [here](https://www.mdpi.com/2072-4292/12/6/1034).\n",
    "\n",
    "There are many platforms to use for gathering land cover training labels, the best one to use depends on your application. GIS platforms are great for collecting training data as they are highly flexible and mature platforms; [Geo-Wiki](https://www.geo-wiki.org/) and [Collect Earth Online](https://collect.earth/home) are two open-source websites that may also be useful depending on the reference data strategy employed. Alternatively, there are many pre-existing training datasets on the web that may be useful, e.g. [Radiant Earth](https://www.radiant.earth/) manages a growing number of reference datasets for use by anyone. With locations of land cover labels available, we can extract features at these locations from satellite imagery as input for machine learning.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7deb13-06df-4f10-b313-9812e2c765ea",
   "metadata": {},
   "source": [
    "## Description\n",
    "\n",
    "As timely training data is not always available, in this notebook we demonstrate how to generate a set of randomly distributed training points for a selected district in Rwanda from an existing classification map.\n",
    "\n",
    "The workflow includes the following steps:\n",
    "\n",
    "1. Preview the district boundaries of Rwanda on a basemap\n",
    "2. Select a district as area of analysis\n",
    "3. Merging classes on the classification map to keep only those you want\n",
    "4. Generate randomly distributed training points and export for future use\n",
    "\n",
    "***\n",
    "\n",
    "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. \n",
    "\n",
    "Note: The approximate running time for the default setting is about 2 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893a84f8-7d62-4dba-af3a-619f3fba0069",
   "metadata": {},
   "source": [
    "### Load packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7428a717-3694-4d1e-b615-c3f5b0ba7cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import rioxarray\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap,BoundaryNorm\n",
    "from matplotlib.patches import Patch\n",
    "from random_sampling import random_sampling # adapted from function by Chad Burton: https://gist.github.com/cbur24/04760d645aa123a3b1817b07786e7d9f\n",
    "from datacube.utils.cog import write_cog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f01ac7-47f3-4c1d-8344-650cd16d3cb1",
   "metadata": {},
   "source": [
    "## Analysis parameters\n",
    "\n",
    "* `input_polygons_path`: The path to the shapefile containing polygons of Rwanda administrative boundaries. Here we use [administrative level 3 boundaries file](https://geoportal.icpac.net/layers/geonode%3Arwa_adm3). \n",
    "* `input_map_path`: The classification map used to extract the training points. Here we use the [Rwanda Land Cover 2015 Scheme II map](http://geoportal.rcmrd.org/layers/servir%3Arwanda_landcover_2015_scheme_ii).\n",
    "* `name_attribute`: This is the name of column in your shapefile attribute table that identifies the name of each polygon at an administrative level.\n",
    ">**Note**: If you change your file to a different shapefile, remember to update this variable to identify your area of interest.\n",
    "* `output_crs`: Output spatial reference system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37c7a9a5-ce0f-4eac-bad6-0896c1aecb21",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_polygons_path = 'Data/rwa_adm3.shp'\n",
    "input_map_path='Data/rwanda_landcover_2015_scheme_ii.tif'\n",
    "name_attribute = 'NAME_3'\n",
    "output_crs='epsg:32735' # WGS84/UTM Zone 35S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267785f6-35fc-4737-a91b-404ea37937df",
   "metadata": {},
   "source": [
    "## Load and display input data\n",
    "Let's load the administration polygons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2e67a5c-65f7-42ee-9768-af3ae1ee6e85",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_0</th>\n",
       "      <th>ISO</th>\n",
       "      <th>NAME_0</th>\n",
       "      <th>ID_1</th>\n",
       "      <th>NAME_1</th>\n",
       "      <th>ID_2</th>\n",
       "      <th>NAME_2</th>\n",
       "      <th>ID_3</th>\n",
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       "      <th>CCA_3</th>\n",
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       "      <td>189</td>\n",
       "      <td>RWA</td>\n",
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       "      <td>4403</td>\n",
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       "      <td>Sector</td>\n",
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       "      <td>None</td>\n",
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       "      <td>189</td>\n",
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       "      <td>1</td>\n",
       "      <td>Amajyaruguru</td>\n",
       "      <td>1</td>\n",
       "      <td>Burera</td>\n",
       "      <td>4</td>\n",
       "      <td>Cyeru</td>\n",
       "      <td>4404</td>\n",
       "      <td>4404</td>\n",
       "      <td>Sector</td>\n",
       "      <td>Sector</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((29.81518 -1.47519, 29.81554 -1.47526...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189</td>\n",
       "      <td>RWA</td>\n",
       "      <td>Rwanda</td>\n",
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       "      <td>Amajyaruguru</td>\n",
       "      <td>1</td>\n",
       "      <td>Burera</td>\n",
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       "      <td>Gahunga</td>\n",
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       "      <td>4405</td>\n",
       "      <td>Sector</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((29.68848 -1.37768, 29.69047 -1.38383...</td>\n",
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       "   ID_0  ISO  NAME_0  ID_1        NAME_1  ID_2  NAME_2  ID_3   NAME_3  CCN_3  \\\n",
       "0   189  RWA  Rwanda     1  Amajyaruguru     1  Burera     1   Bungwe   4401   \n",
       "1   189  RWA  Rwanda     1  Amajyaruguru     1  Burera     2   Butaro   4402   \n",
       "2   189  RWA  Rwanda     1  Amajyaruguru     1  Burera     3  Cyanika   4403   \n",
       "3   189  RWA  Rwanda     1  Amajyaruguru     1  Burera     4    Cyeru   4404   \n",
       "4   189  RWA  Rwanda     1  Amajyaruguru     1  Burera     5  Gahunga   4405   \n",
       "\n",
       "  CCA_3  TYPE_3 ENGTYPE_3 NL_NAME_3 VARNAME_3  \\\n",
       "0  4401  Sector    Sector      None      None   \n",
       "1  4402  Sector    Sector      None      None   \n",
       "2  4403  Sector    Sector      None      None   \n",
       "3  4404  Sector    Sector      None      None   \n",
       "4  4405  Sector    Sector      None      None   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((29.98542 -1.45859, 29.98555 -1.45892...  \n",
       "1  POLYGON ((29.85182 -1.34369, 29.85192 -1.34424...  \n",
       "2  POLYGON ((29.73102 -1.34058, 29.73149 -1.34087...  \n",
       "3  POLYGON ((29.81518 -1.47519, 29.81554 -1.47526...  \n",
       "4  POLYGON ((29.68848 -1.37768, 29.69047 -1.38383...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygons=gpd.read_file(input_polygons_path)\n",
    "polygons.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a54c12-27ed-42c5-ae6f-bff509f1aab2",
   "metadata": {},
   "source": [
    "Let's load the classification map and display it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6252bf0-6c41-481e-8d13-88151b02f871",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_map=xr.open_dataset(input_map_path,engine=\"rasterio\").astype(np.uint8)\n",
    "classification_map=classification_map.to_array().squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a43f75c-0a0d-462f-a0f3-c56cb8cfeee7",
   "metadata": {},
   "source": [
    "According to its metadata of the classification map, it conatains 15 classes including Nodata represented by its pixel values: Nodata (0), Dense Forest (1), Moderate Forest (2), Sparse Forest (3), Woodland (4), Closed Grassland (5), Open Grassland (6), Closed Shrubland (7), Open Shrubland (8), Perennial Cropland (9), Annual Cropland (10), Wetland (11), Water Body (12), Urban Settlement (13) and Other Land (14). Here we define a dictionary of class name corresponding to pixel values for dispaly:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5138a0b-90a3-4879-9ef8-2ac972a734c9",
   "metadata": {},
   "source": [
    ">**Note**: If you change the classification map, you will need to understand what class each pixel value represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6b82305-cb3b-4f60-8b00-232be9f2bee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f63befbc760>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_map={'Nodata':0,'Dense Forest':1,'Moderate Forest':2,'Sparse Forest':3,'Woodland':4,\n",
    "          'Closed Grassland':5,'Open Grassland':6,'Closed Shrubland':7,'Open Shrubland':8,\n",
    "          'Perennial Cropland':9,'Annual Cropland':10,'Wetland':11,'Water Body':12,'Urban Settlement':13,'Other Land':14}\n",
    "# display colour for each class value\n",
    "colours = {0:'white',1:'darkgreen',2:'limegreen',3:'lime',4:'lightgreen',5:'olive',6:'yellow',7:'goldenrod',\n",
    "           8:'darkorange',9:'magenta',10:'pink',11:'cyan',12:'blue',13:'gray',14:'black'}\n",
    "\n",
    "fig, axes = plt.subplots(1,1)\n",
    "\n",
    "# Plot classification map\n",
    "unique_values=np.unique(classification_map)\n",
    "cmap=ListedColormap([colours[k] for k in unique_values])\n",
    "norm = BoundaryNorm(list(unique_values)+[np.max(unique_values)+1], cmap.N)\n",
    "classification_map.plot.imshow(ax=axes, \n",
    "                   cmap=cmap,\n",
    "                   norm=norm,\n",
    "                   add_labels=True, \n",
    "                   add_colorbar=False,\n",
    "                   interpolation='none')\n",
    "# add colour legend\n",
    "patches_list=[Patch(facecolor=colour) for colour in colours.values()]\n",
    "axes.legend(patches_list, list(dict_map.keys()),loc='upper center', ncol =4, bbox_to_anchor=(0.5, -0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8006182-662b-4ac2-86dd-ec0d8520a4ba",
   "metadata": {},
   "source": [
    "## Select district for analysis\n",
    "Now we select a district of interest for analysis. Here we select the district `Nyarugunga` and will use this district as demonstration for the rest of the workflow.  \n",
    ">**Note**: If you change your district of interest, depending on its area size you may expect more time and need more memory to process. Meanwhile we recommend you select a region that has various classes as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "157017a8-5182-48e8-8f78-ab60dc5923b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "district_name='Mwogo'\n",
    "polygon=polygons.loc[polygons[name_attribute]==district_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e88878-259e-454f-91ac-ca8cb3f3f9de",
   "metadata": {},
   "source": [
    "You can display the selected polygon on an interactive basemap. When you hover on each polygon you would be able to its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e5fe385-faa4-4fac-9bba-9ae04c5ed077",
   "metadata": {},
   "outputs": [
    {
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-2.0476851463316734], [30.130632400512752, -2.047266721725407], [30.130830764770565, -2.0466489791870117], [30.131467819213924, -2.045463562011605], [30.131977081298885, -2.0445868968962486], [30.13232612609869, -2.044297933578264], [30.132902145385685, -2.0438594818115234], [30.13336181640642, -2.043879508972168], [30.133670806884766, -2.0440089702605633], [30.133819580078352, -2.044417381286621], [30.13411903381359, -2.0453639030455406], [30.13414764404297, -2.0458519458770184], [30.134187698364258, -2.0465095043182373], [30.134326934814737, -2.047027587890625], [30.13445663452154, -2.047475814819279], [30.13472557067894, -2.047645330429077], [30.135173797607592, -2.047236680984497], [30.135423660278605, -2.0468981266020023], [30.136032104492358, -2.046529531478882], [30.136737823486385, -2.046240568160897], [30.137117385864315, -2.0460910797118004], [30.137287139892578, -2.0458321571350098], [30.137296676635913, -2.0454037189483074], [30.137077331543026, -2.045144796371403], [30.13666915893549, -2.044965505599748], [30.135931015014762, -2.04453706741333], [30.134984970092887, -2.0436604022979736], [30.134775161743164, -2.0432817935943035], [30.13468551635748, -2.0429530143737225], [30.134984970092887, -2.0427937507628258], [30.135244369506836, -2.042684078216382], [30.135723114013842, -2.042803525924569], [30.136110305786133, -2.04298281669611], [30.13658905029314, -2.0433316230773357], [30.137147903442667, -2.0436902046203613], [30.13755607604986, -2.0438296794891357], [30.137973785400675, -2.0436303615567795], [30.1382026672365, -2.0433316230773357], [30.13828277587885, -2.043062686920166], [30.13816261291521, -2.042295455932617], [30.137674331665266, -2.0415582656859783], [30.136877059936808, -2.040552139282113], [30.13698768615734, -2.039775133132821], [30.13724708557129, -2.039546012878418], [30.1374454498291, -2.0394363403319744], [30.13774490356451, -2.039535999298039], [30.138053894043253, -2.0399343967436607], [30.13854217529297, -2.0404624938964275], [30.1391792297365, -2.0407912731170654], [30.139657974243278, -2.040751457214128], [30.14018630981451, -2.0404226779936607], [30.140523910522404, -2.040163516998234], [30.140882492065714, -2.039585828781128], [30.141111373901538, -2.039077758788949], [30.141450881958008, -2.038490056991577], [30.141738891601733, -2.038300752639657], [30.14201927185087, -2.0383803844450767], [30.142217636108683, -2.0386891365048996], [30.14226722717291, -2.0391774177550133], [30.14226722717291, -2.039665460586548], [30.1424179077149, -2.0403029918670654], [30.142467498779354, -2.040681600570565], [30.142686843872355, -2.041060209274292], [30.14331436157255, -2.040980577468872], [30.14377212524414, -2.0408110618589035], [30.143941879272575, -2.0407214164733887], [30.145046234130973, -2.0399544239043053], [30.14556503295904, -2.0395259857177734], [30.146121978759993, -2.0394065380095867], [30.146471023559627, -2.0395658016204266], [30.146671295166072, -2.039904594421273], [30.14698028564476, -2.0406217575071537], [30.14721870422386, -2.040940761566162], [30.14757728576666, -2.040781259536516], [30.147985458374194, -2.040233373641911], [30.14855384826683, -2.039535999298039], [30.14897155761713, -2.038778781890869], [30.149354934692667, -2.0381693840026855], [30.149423599243335, -2.038059711456299], [30.149459838867415, -2.038001775741577], [30.149538040161417, -2.0379414558409508], [30.149818420410213, -2.0377228260039715], [30.15040588378929, -2.0376830101012047], [30.150688171386662, -2.037675142288151], [30.151123046875, -2.03766298294056], [30.151512145996094, -2.0373144149779705], [30.15171241760271, -2.036955833434945], [30.151651382446573, -2.035840034484636], [30.15188026428217, -2.035182476043701], [30.152318954468058, -2.034604787826538], [30.152738571166935, -2.0343656539915855], [30.152929306030217, -2.0343897342681885], [30.15360450744646, -2.0344750881192795], [30.154253005981502, -2.0347442626952557], [30.15464019775419, -2.035003185272217], [30.154998779296932, -2.0354514122009277], [30.155288696289233, -2.035830020904541], [30.155666351318416, -2.036118984222412], [30.156534194946346, -2.036079168319702], [30.15719032287626, -2.0358598232269287], [30.157539367675895, -2.0356209278104984], [30.15790748596197, -2.0350131988524254], [30.157978057861612, -2.0344452857968918], [30.15813827514671, -2.034026861190796], [30.15813827514671, -2.0334191322325523], [30.158008575439567, -2.032671928405648], [30.157897949219034, -2.0320842266082195], [30.15795898437517, -2.0307991504668053], [30.158346176147518, -2.030809164047241], [30.15867614746105, -2.0310282707213787], [30.159671783447322, -2.031646013259831], [30.160448074340934, -2.0319547653197674], [30.16110610961931, -2.0320742130278404], [30.16160392761236, -2.032024383544808], [30.16194343566923, -2.031785488128662], [30.162073135376033, -2.031476497650033], [30.162023544311808, -2.0308687686920166], [30.16196250915533, -2.0299623012542156], [30.162033081054744, -2.0295836925506023], [30.162326812744368, -2.0295028686523438], [30.162431716918945, -2.029474258422624], [30.162750244140625, -2.029783010482788], [30.16301918029791, -2.0302312374112716], [30.16321754455572, -2.0308489799498375], [30.163177490234432, -2.0315661430358887], [30.163408279419002, -2.032433032989502], [30.163866043091105, -2.0331501960753826], [30.164583206176815, -2.0336880683898926], [30.165189743041992, -2.0338375568389893], [30.16558837890642, -2.0338773727416424], [30.16614723205595, -2.033737897872925], [30.166536331177042, -2.0333096981047447], [30.16660499572771, -2.0328414440153892], [30.166574478149585, -2.0324528217315105], [30.166496276855582, -2.0309386253356934], [30.166465759277628, -2.030350923538151], [30.16666412353544, -2.030051946639901], [30.166904449463118, -2.029972314834481], [30.167303085327205, -2.029932498931828], [30.168239593506087, -2.030111789703369], [30.169105529785384, -2.0303409099578857], [30.17002296447771, -2.0306496620177086], [30.170619964599553, -2.0308687686920166], [30.171167373657397, -2.0311379432678223], [30.171646118164176, -2.03127741813654], [30.171945571899585, -2.0313072204589275], [30.17213439941412, -2.0312275886535076], [30.172143936157227, -2.0310282707213787], [30.171934127807674, -2.030470371246338], [30.171726226806925, -2.0300719738005455], [30.171627044677734, -2.029783010482788], [30.171745300293026, -2.0291054248809814], [30.17215347290056, -2.02842807769764], [30.17257690429716, -2.028231143951416], [30.173021316528605, -2.028258800506535], [30.17348861694353, -2.028577566146737], [30.173837661743335, -2.0290656089782715], [30.17418670654314, -2.029623508453369], [30.17452430725126, -2.0300817489622887], [30.174833297729776, -2.030619859695321], [30.175033569335994, -2.031287193298283]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;197&quot;, &quot;properties&quot;: {&quot;CCA_3&quot;: &quot;5707&quot;, &quot;CCN_3&quot;: 5707, &quot;ENGTYPE_3&quot;: &quot;Sector&quot;, &quot;ID_0&quot;: 189, &quot;ID_1&quot;: 3, &quot;ID_2&quot;: 14, &quot;ID_3&quot;: 198, &quot;ISO&quot;: &quot;RWA&quot;, &quot;NAME_0&quot;: &quot;Rwanda&quot;, &quot;NAME_1&quot;: &quot;Iburasirazuba&quot;, &quot;NAME_2&quot;: &quot;Bugesera&quot;, &quot;NAME_3&quot;: &quot;Mwogo&quot;, &quot;NL_NAME_3&quot;: null, &quot;TYPE_3&quot;: &quot;Sector&quot;, &quot;VARNAME_3&quot;: null}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_d1630d8dd5c6eb82d4c49951c7ff1821.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;ID_0&quot;, &quot;ISO&quot;, &quot;NAME_0&quot;, &quot;ID_1&quot;, &quot;NAME_1&quot;, &quot;ID_2&quot;, &quot;NAME_2&quot;, &quot;ID_3&quot;, &quot;NAME_3&quot;, &quot;CCN_3&quot;, &quot;CCA_3&quot;, &quot;TYPE_3&quot;, &quot;ENGTYPE_3&quot;, &quot;NL_NAME_3&quot;, &quot;VARNAME_3&quot;];\n",
       "    let aliases = [&quot;ID_0&quot;, &quot;ISO&quot;, &quot;NAME_0&quot;, &quot;ID_1&quot;, &quot;NAME_1&quot;, &quot;ID_2&quot;, &quot;NAME_2&quot;, &quot;ID_3&quot;, &quot;NAME_3&quot;, &quot;CCN_3&quot;, &quot;CCA_3&quot;, &quot;TYPE_3&quot;, &quot;ENGTYPE_3&quot;, &quot;NL_NAME_3&quot;, &quot;VARNAME_3&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
       "                     \n",
       "    \n",
       "    geo_json_d1630d8dd5c6eb82d4c49951c7ff1821.bindPopup(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;ID_0&quot;, &quot;ISO&quot;, &quot;NAME_0&quot;, &quot;ID_1&quot;, &quot;NAME_1&quot;, &quot;ID_2&quot;, &quot;NAME_2&quot;, &quot;ID_3&quot;, &quot;NAME_3&quot;, &quot;CCN_3&quot;, &quot;CCA_3&quot;, &quot;TYPE_3&quot;, &quot;ENGTYPE_3&quot;, &quot;NL_NAME_3&quot;, &quot;VARNAME_3&quot;];\n",
       "    let aliases = [&quot;ID_0&quot;, &quot;ISO&quot;, &quot;NAME_0&quot;, &quot;ID_1&quot;, &quot;NAME_1&quot;, &quot;ID_2&quot;, &quot;NAME_2&quot;, &quot;ID_3&quot;, &quot;NAME_3&quot;, &quot;CCN_3&quot;, &quot;CCA_3&quot;, &quot;TYPE_3&quot;, &quot;ENGTYPE_3&quot;, &quot;NL_NAME_3&quot;, &quot;VARNAME_3&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i].toLocaleString()}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v]).toLocaleString()}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumpopup&quot;});\n",
       "                     \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f641024deb0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygon.explore(\n",
    "    tiles = \"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\", \n",
    "    attr ='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
    "    popup=True,\n",
    "    cmap='viridis',\n",
    "    style_kwds=dict(color= 'red', fillOpacity= 0, weight= 3),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17066fb7-2826-43ef-87de-6e225a4a39be",
   "metadata": {},
   "source": [
    "With the district selected, we clip the classification map to the region for later analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c766133-972f-4f75-a627-1679ea7e9794",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_clipped=classification_map.rio.clip(geometries=polygon.geometry.values, crs=polygon.crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4482b30-82f6-4f78-b1b3-eb3dd3a5dc54",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Class merging\n",
    "\n",
    "As we would like to use training samples for pure pixels as possible but the classification map contains more class than we want, we abandon classes legends that are likely mixture of pure classes, e.g. Sparse Forest and Moderate Forest and rename pure clases:  \n",
    "* Dense Forest (1) as Forest (1);  \n",
    "* Closed Grassland (5) as Grassland (5);  \n",
    "* Closed Shrubland (7) as Shrubland (7);  \n",
    "* Other Land (14) as Bare Land (14).\n",
    "\n",
    "Here we abandon the classes Moderate Forest (2), Sparse Forest (3), Woodland (4), Open Grassland (6) and Open Shrubland (8) from the map to be used for training data generation, so set them as Nodata (0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a280fb0d-5450-45f4-b48b-987370e65249",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_clipped=map_clipped.where((map_clipped!=dict_map['Moderate Forest'])\n",
    "                              &(map_clipped!=dict_map['Sparse Forest'])\n",
    "                              &(map_clipped!=dict_map['Woodland'])\n",
    "                              &(map_clipped!=dict_map['Open Grassland'])\n",
    "                              &(map_clipped!=dict_map['Open Shrubland']),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb28132-c609-4ed4-a29d-4b459dca93b5",
   "metadata": {},
   "source": [
    ">**Note**: If you change your class merging strategy, some of the variables related to class names and values used in the subsequent notebooks need to be updated as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49952952-0c9c-4108-b73e-c58e29918267",
   "metadata": {},
   "source": [
    "## Generate random training samples\n",
    "We generate some randomly distributed samples for each class from the clipped classification map using the `random_sampling` function. This function takes in a few parameters:  \n",
    "* `da`: a classified map in the format of 2-dimensional xarray.DataArray\n",
    "* `n`: total number of points to sample.\n",
    "* `min_sample_n`: Minimum number of samples to generate per class if proportional number is smaller than this. **Note that the resultant number of samples may be higher than the set `n` due to setting of this minimum number of samples.** \n",
    "* `sampling`: the sampling strategy, e.g. 'stratified_random' where each class has a number of points proportional to its relative area, 'equal_stratified_random' where each class has the same number of points, or 'manual' which allows you to define number of samples for each class.\n",
    "* `out_fname`: a filepath name for the function to export a shapefile/geojson of the sampling points into a file. You can set this to `None` if you don't need to output the file.\n",
    "* `class_attr`: This is the column name of output dataframe that contains the integer class values on the classified map. \n",
    "* `drop_value`: pixel value on the classification map to be excluded from sampling.  \n",
    "\n",
    "The output of the function is a geopandas dataframe of randomly distributed points containing a column `class_attr` identifying class values. \n",
    "\n",
    "Here we extract around 50 training points in total and export the points in a geojson file for use in the rest of workflow. Here we use the stratified sampling method by setting 'equal_stratified_random', but also set the minimum number of samples as 3 to avoid missing samples for some minor classes. \n",
    "\n",
    "As mentioned earlier we don't want the abandoned classes to be included in the samples we set drop_value as 0 before implementing the function:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ba0cdb-61ee-401b-bfd0-0b2254a72db3",
   "metadata": {},
   "source": [
    "We now implement the function and set the output file name incorporating the district name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5992659-25c7-484d-9861-432f433744e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 10: sampling at 35locations\n",
      "Class 11: sampling at 12locations\n",
      "Class 12: sampling at 3locations\n",
      "Class 13: sampling at 3locations\n",
      "Class 7: sampling at 3locations\n",
      "Class 14: sampling at 3locations\n",
      "Class 9: sampling at 3locations\n"
     ]
    }
   ],
   "source": [
    "class_attr='LC_Class_I'\n",
    "out_fname='Results/Training_samples.geojson'\n",
    "n=50\n",
    "min_sample_n=3\n",
    "gpd_random_samples=random_sampling(da=map_clipped,n=n,sampling='stratified_random',\n",
    "                                   min_sample_n=min_sample_n,out_fname=out_fname,class_attr=class_attr,drop_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60749f18-6691-4cbd-9a63-55ab98eefcf0",
   "metadata": {},
   "source": [
    ">**Note**: The output training data file can also be in other formats (e.g. shapefile) that can be read by `geopandas`, but if you change it, remember to update wherever it was used in subsequent notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d424c6e9-3893-499d-8c5a-6c6f9865af0f",
   "metadata": {},
   "source": [
    "Finally, we also export the clipped and class-merged map to disk for future use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82e0cdbd-0bb9-44f7-8ca4-2430b00c3a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Results/rwanda_landcover_2015_scheme_ii_clipped.tif')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outname='Results/rwanda_landcover_2015_scheme_ii_clipped.tif'\n",
    "write_cog(map_clipped, outname, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163289b6-3065-433f-aa4e-cbd19fc7c444",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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