notebook_LiveEO_Challenge_Thibault_Latrace.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LiveEO Data Science Challenge - Thibault Latrace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Sentinel-2 satellite](resources/sentinel-2_image.jpg)\n",
    "\n",
    "This notebook aims at solving the LiveEO Sentinel-2 challenge, whose wording can be found in the GitHub repository.\n",
    "\n",
    "The goal is to implement a machine learning model able to predict the type of a geographic area (forest, water, urban...) from Sentinel-2 satellite image scenes. The Sentinel-2 (S-2) satellite is equipped with a Multi-Spectral Instrument (MSI) which can provide up to 15 spectral bands ranging from the visible range to the shortwave infrared, plus additional band outputs. These bands will be our features in this study.\n",
    "\n",
    "My **problem solving stategy** is the following : \n",
    "\n",
    "- Dataset exploration\n",
    "- Classifier training\n",
    "- Model accuracy evalutation & limits\n",
    "\n",
    "Let's dive into it !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Dataset exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import of the libraries and the tools that will be used during the whole study :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "import folium\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import scikitplot as skplt\n",
    "\n",
    "from pyproj import Transformer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploration of the labeled dataset metadata :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset metadata : \n",
      "\n",
      "{'driver': 'GTiff', 'dtype': 'uint32', 'nodata': 4294967295.0, 'width': 10980, 'height': 10980, 'count': 2, 'crs': CRS.from_epsg(32617), 'transform': Affine(10.0, 0.0, 300000.0,\n",
      "       0.0, -10.0, 4400040.0)}\n",
      "\n",
      "\n",
      "CRS : EPSG:32617\n",
      "Shape : (10980, 10980)\n",
      "Number of bands : 2\n",
      "Type of the first layer : uint32\n",
      "Nodata value : 4294967295.0\n"
     ]
    }
   ],
   "source": [
    "dataset = rasterio.open('resources/rasterize_on_sentinel_2_labelled_17SLD.tif')\n",
    "\n",
    "print('Dataset metadata : \\n\\n'+ str(dataset.meta))\n",
    "\n",
    "print('\\n\\nCRS : ' + str(dataset.meta['crs']))\n",
    "print('Shape : ' + str(dataset.shape))\n",
    "print('Number of bands : ' + str(dataset.count))\n",
    "print('Type of the first layer : ' + str(list(dataset.dtypes)[0]))\n",
    "print('Nodata value : ' + str(dataset.nodata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the Coordinates Reference System is EPSG:32617, which corresponds to the **UTM zone 17N** according to the documentation of the [epsg.io website](https://epsg.io/32617)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to have a precise overview of the problem, we should see on a map what zone of the world is on the picture. Let's see what area this picture delimits :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use of the metada affine transformation matrix to spot the spatial position of the four picture corners in the crs 32617\n",
    "previous_upper_left_corner_coords = dataset.transform * (0, 0)\n",
    "previous_upper_right_corner_coords = dataset.transform * (dataset.width, 0)\n",
    "previous_lower_left_corner_coords = dataset.transform * (0, dataset.height)\n",
    "previous_lower_right_corner_coords = dataset.transform * (dataset.width, dataset.height)\n",
    "\n",
    "# transformer implementation to switch the crs to the classic WGS 84 CRS\n",
    "utm_zone_17N_crs = 32617\n",
    "WGS84_crs = 4326\n",
    "transformer = Transformer.from_crs(utm_zone_17N_crs, WGS84_crs)\n",
    "\n",
    "# corners coordinates conversion\n",
    "corners = [\n",
    "    previous_upper_left_corner_coords,\n",
    "    previous_upper_right_corner_coords,\n",
    "    previous_lower_left_corner_coords,\n",
    "    previous_lower_right_corner_coords\n",
    "]\n",
    "corners_coords = list()\n",
    "for point in transformer.itransform(corners):\n",
    "    coords = '{} {}'.format(*point)\n",
    "    x = coords.split(' ')[0]\n",
    "    y = coords.split(' ')[1]\n",
    "    corners_coords.append((x, y))\n",
    "new_upper_left_corner_coords = corners_coords[0]\n",
    "new_upper_right_corner_coords = corners_coords[1]\n",
    "new_lower_left_corner_coords = corners_coords[2]\n",
    "new_lower_right_corner_coords = corners_coords[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's watch on an interactive map to what area corresponds the image :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1e30d654ac8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creation of a folium interactive map\n",
    "m = folium.Map(list(new_lower_right_corner_coords),zoom_start = 6, tiles='Stamen Terrain')\n",
    "folium.Marker(list(new_upper_left_corner_coords)).add_to(m)\n",
    "folium.Marker(list(new_upper_right_corner_coords)).add_to(m)\n",
    "folium.Marker(list(new_lower_left_corner_coords)).add_to(m)\n",
    "folium.Marker(list(new_lower_right_corner_coords)).add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Our area is located in the square delimited by the four corner markers. It is next to **Columbus** (Ohio) in the United States.* It is consistent because this area is in the scope of UTM zone 17N."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize the dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e30d64b6c8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lfp+PiNqE6yepXrl5K/D9dBsatb/e7olR6ydzBkdEXNtg07o6fQO4ocHj7AJ21WmfAN4/Vx2DzKFh/cbfHDWzbA4OM8vm4DCzbA4OM8vm4CgBX5K1fuPgKAFfVbF+4+AogRHJow7rK3N+j8M6zyMO6zcecfSYRxrWjxwcJeDwsH7jU5Ue82mK9SOPOMwsm4Ojh2qnKD5VsX7j4Oghn6ZYv1L06YtX0svAkV7X0cC7gBd7XcQsylxfmWuD4arv9yKi7u/o7OfJ0SMRUel1EfVImihrbVDu+spcG7i+Gp+qmFk2B4eZZevn4NjR6wJmUebaoNz1lbk2cH1AH0+Omlnv9POIw8x6xMFhZtn6LjgkbZR0RNKkpG1des6Vkh6QdFjSE5I+ldqXSjog6Wi6X5LaJemWVOOjktYUHms89T8qabzRczZZ5wJJj0i6N62vlnQoPdedkhal9rPS+mTavqrwGNtT+xFJG9pY22JJeyU9lY7jZWU5fpI+k/5dH5d0h6Sze3nsJO2SdELS44W2th0rSRdLeiztc0v6Y2h5IqJvbsAC4KfA+cAi4H+BC7vwvMuANWn5d4GfABcC/whsS+3bgK+k5Sup/mEpAWuBQ6l9KfB0ul+Slpe0sc6/Af4VuDet3wVsTsvfAD6Zlv8C+EZa3gzcmZYvTMf0LGB1OtYL2lTbbuDP0/IiYHEZjh/VP3L+DPDWwjH7014eO+CDwBrg8UJb244V8EPgsrTP94Ersmvs9Juunbf0w+4vrG8HtvegjnuAj1L95uqy1LaM6pfSAL4JXFvofyRtvxb4ZqH9Df1arGkFcBD4MHBvelG8CCyceeyo/knOy9LywtRPM49nsV+Ltb09vTk1o73nxy8Fx7H0BluYjt2GXh87YNWM4GjLsUrbniq0v6HffG/9dqpS+0eumUptXZOGphcBh4Dzovr3b0n356ZujersZP03A58FfpPW3wm8FBFn6jzX63Wk7adT/07Vdz4wDXw7nUp9S9IIJTh+EfEz4KtU/5Tp81SPxcOU59jVtOtYLU/LLdXZb8FR71ysa9eTJb0N+B7w6Yj4xWxd67TFLO2t1vUx4EREPDyPGmbb1qnju5Dq0PvWiLgIeIXqcLuRrtWX5go2UT29eDcwAlwxy/N0+9jNJbeettTZb8ExBawsrK8AjnfjiSW9hWpofDci7k7NL0halrYvA07MUWen6r8c+LikZ4E9VE9XbgYWS6r9f6Tic71eR9r+DuBkB+ubAqYi4lBa30s1SMpw/D4CPBMR0xHxGnA38AHKc+xq2nWsptJyS3X2W3A8BIylGe9FVCen9nX6SdOs807gcER8rbBpH1CbrR6nOvdRa78uzXivBU6n4eV+YL2kJemTbn1qa0lEbI+IFRGxiuoxuT8iPgE8AFzdoL5a3Ven/pHaN6crB6uBMaoTaa3W93PgmKT3pqZ1wJOU4/g9B6yVdE76d67VVopjV9CWY5W2vSxpbfp5rys81vy1OvHV7RvVWeSfUJ21/lyXnvOPqA7nHgV+nG5XUj23PQgcTfdLU38B/5JqfAyoFB7rz4DJdLu+A7V+iN9eVTmf6ot3Evg34KzUfnZan0zbzy/s/7lU9xGamG2fpa4/BCbSMfx3qjP9pTh+wD8ATwGPA7dTvTLSs2MH3EF1vuU1qiOELe08VkAl/aw/Bf6ZGZPW87n5K+dmlq3fTlXMrAQcHGaWzcFhZtkcHGaWzcFhZtkcHGaWzcFhZtn+H3c2ZWHu1lJsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_band = dataset.read(1)\n",
    "\n",
    "plt.imshow(labeled_band, cmap='hot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the dots are the labelled parts. We can see that the majority of the data are nodata values !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now transform the labelled band into a DataFrame in order to ease the manipulation,by dropping the label nodata values : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduction coefficient :369.0\n"
     ]
    }
   ],
   "source": [
    "reduced_label_band_df = pd.DataFrame(labeled_band.ravel()).replace(4294967295, pd.NA)\n",
    "previous_label_band_size = reduced_label_band_df.shape\n",
    "\n",
    "# this is the key step where we drop nodata values\n",
    "reduced_label_band_df = reduced_label_band_df.dropna().rename(columns = {0: 'label'})\n",
    "new_label_band_size = reduced_label_band_df.shape\n",
    "\n",
    "# forcing the label to int value\n",
    "reduced_label_band_df['label'] = reduced_label_band_df['label'].astype(int)\n",
    "\n",
    "print('Reduction coefficient :' + str(np.round(previous_label_band_size[0]/new_label_band_size[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just **divided by almost 400 the size** of our dataset !!!\n",
    "It will be a lot more convenient to work with this size-reduced data for the machine learning process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenation of the labeled layer and the 15 bands of the dataset, by projecting the bands where the labeled data is not null : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           label  AOT\n",
      "350913         3   69\n",
      "354514         0   70\n",
      "354515         0   70\n",
      "354516         0   70\n",
      "354517         0   70\n",
      "...          ...  ...\n",
      "120332403      4   70\n",
      "120409747      3   70\n",
      "120420734      3   70\n",
      "120464649      3   70\n",
      "120519538      3   70\n",
      "\n",
      "[326391 rows x 2 columns]\n",
      "           label  AOT   B01\n",
      "350913         3   69   360\n",
      "354514         0   70  2347\n",
      "354515         0   70  2347\n",
      "354516         0   70  3878\n",
      "354517         0   70  3878\n",
      "...          ...  ...   ...\n",
      "120332403      4   70   264\n",
      "120409747      3   70   589\n",
      "120420734      3   70   760\n",
      "120464649      3   70   768\n",
      "120519538      3   70   442\n",
      "\n",
      "[326391 rows x 3 columns]\n",
      "           label  AOT   B01   B02\n",
      "350913         3   69   360   391\n",
      "354514         0   70  2347  2302\n",
      "354515         0   70  2347  4328\n",
      "354516         0   70  3878  4316\n",
      "354517         0   70  3878  4460\n",
      "...          ...  ...   ...   ...\n",
      "120332403      4   70   264   236\n",
      "120409747      3   70   589   322\n",
      "120420734      3   70   760   713\n",
      "120464649      3   70   768   517\n",
      "120519538      3   70   442   329\n",
      "\n",
      "[326391 rows x 4 columns]\n",
      "           label  AOT   B01   B02   B03\n",
      "350913         3   69   360   391   640\n",
      "354514         0   70  2347  2302  2674\n",
      "354515         0   70  2347  4328  4272\n",
      "354516         0   70  3878  4316  4564\n",
      "354517         0   70  3878  4460  4400\n",
      "...          ...  ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466\n",
      "120409747      3   70   589   322   462\n",
      "120420734      3   70   760   713   939\n",
      "120464649      3   70   768   517   576\n",
      "120519538      3   70   442   329   530\n",
      "\n",
      "[326391 rows x 5 columns]\n",
      "           label  AOT   B01   B02   B03   B04\n",
      "350913         3   69   360   391   640   735\n",
      "354514         0   70  2347  2302  2674  2778\n",
      "354515         0   70  2347  4328  4272  4220\n",
      "354516         0   70  3878  4316  4564  4728\n",
      "354517         0   70  3878  4460  4400  4792\n",
      "...          ...  ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263\n",
      "120409747      3   70   589   322   462   473\n",
      "120420734      3   70   760   713   939   991\n",
      "120464649      3   70   768   517   576   686\n",
      "120519538      3   70   442   329   530   438\n",
      "\n",
      "[326391 rows x 6 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05\n",
      "350913         3   69   360   391   640   735  1274\n",
      "354514         0   70  2347  2302  2674  2778  3944\n",
      "354515         0   70  2347  4328  4272  4220  3944\n",
      "354516         0   70  3878  4316  4564  4728  5445\n",
      "354517         0   70  3878  4460  4400  4792  5445\n",
      "...          ...  ...   ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263   702\n",
      "120409747      3   70   589   322   462   473  1077\n",
      "120420734      3   70   760   713   939   991  1288\n",
      "120464649      3   70   768   517   576   686  1084\n",
      "120519538      3   70   442   329   530   438   949\n",
      "\n",
      "[326391 rows x 7 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06\n",
      "350913         3   69   360   391   640   735  1274  1954\n",
      "354514         0   70  2347  2302  2674  2778  3944  4185\n",
      "354515         0   70  2347  4328  4272  4220  3944  4185\n",
      "354516         0   70  3878  4316  4564  4728  5445  5242\n",
      "354517         0   70  3878  4460  4400  4792  5445  5242\n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263   702  2844\n",
      "120409747      3   70   589   322   462   473  1077  1570\n",
      "120420734      3   70   760   713   939   991  1288  1654\n",
      "120464649      3   70   768   517   576   686  1084  1603\n",
      "120519538      3   70   442   329   530   438   949  1697\n",
      "\n",
      "[326391 rows x 8 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07\n",
      "350913         3   69   360   391   640   735  1274  1954  2300\n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975\n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975\n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489\n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489\n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263   702  2844  3513\n",
      "120409747      3   70   589   322   462   473  1077  1570  1836\n",
      "120420734      3   70   760   713   939   991  1288  1654  1832\n",
      "120464649      3   70   768   517   576   686  1084  1603  1857\n",
      "120519538      3   70   442   329   530   438   949  1697  1891\n",
      "\n",
      "[326391 rows x 9 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374\n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784\n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220\n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500\n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600\n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340\n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596\n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780\n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064\n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992\n",
      "\n",
      "[326391 rows x 10 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620\n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979\n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979\n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836\n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836\n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834\n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835\n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812\n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022\n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924\n",
      "\n",
      "[326391 rows x 11 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09  \\\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620   \n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979   \n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979   \n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836   \n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836   \n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834   \n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835   \n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812   \n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022   \n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924   \n",
      "\n",
      "            B11  \n",
      "350913     2853  \n",
      "354514     3758  \n",
      "354515     3758  \n",
      "354516     4589  \n",
      "354517     4589  \n",
      "...         ...  \n",
      "120332403  1826  \n",
      "120409747  1815  \n",
      "120420734  1817  \n",
      "120464649  2193  \n",
      "120519538  1891  \n",
      "\n",
      "[326391 rows x 12 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09  \\\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620   \n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979   \n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979   \n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836   \n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836   \n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834   \n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835   \n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812   \n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022   \n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924   \n",
      "\n",
      "            B11   B12  \n",
      "350913     2853  1803  \n",
      "354514     3758  2708  \n",
      "354515     3758  2708  \n",
      "354516     4589  2917  \n",
      "354517     4589  2917  \n",
      "...         ...   ...  \n",
      "120332403  1826   835  \n",
      "120409747  1815  1329  \n",
      "120420734  1817  1621  \n",
      "120464649  2193  1752  \n",
      "120519538  1891  1408  \n",
      "\n",
      "[326391 rows x 13 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09  \\\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620   \n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979   \n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979   \n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836   \n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836   \n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834   \n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835   \n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812   \n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022   \n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924   \n",
      "\n",
      "            B11   B12   B8A  \n",
      "350913     2853  1803  2566  \n",
      "354514     3758  2708  3884  \n",
      "354515     3758  2708  3884  \n",
      "354516     4589  2917  5310  \n",
      "354517     4589  2917  5310  \n",
      "...         ...   ...   ...  \n",
      "120332403  1826   835  3809  \n",
      "120409747  1815  1329  1947  \n",
      "120420734  1817  1621  1801  \n",
      "120464649  2193  1752  1983  \n",
      "120519538  1891  1408  2117  \n",
      "\n",
      "[326391 rows x 14 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09  \\\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620   \n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979   \n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979   \n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836   \n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836   \n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834   \n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835   \n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812   \n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022   \n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924   \n",
      "\n",
      "            B11   B12   B8A  SCL  \n",
      "350913     2853  1803  2566    4  \n",
      "354514     3758  2708  3884    9  \n",
      "354515     3758  2708  3884    9  \n",
      "354516     4589  2917  5310    9  \n",
      "354517     4589  2917  5310    9  \n",
      "...         ...   ...   ...  ...  \n",
      "120332403  1826   835  3809    4  \n",
      "120409747  1815  1329  1947    4  \n",
      "120420734  1817  1621  1801    5  \n",
      "120464649  2193  1752  1983    5  \n",
      "120519538  1891  1408  2117    4  \n",
      "\n",
      "[326391 rows x 15 columns]\n",
      "           label  AOT   B01   B02   B03   B04   B05   B06   B07   B08   B09  \\\n",
      "350913         3   69   360   391   640   735  1274  1954  2300  2374  2620   \n",
      "354514         0   70  2347  2302  2674  2778  3944  4185  3975  2784  2979   \n",
      "354515         0   70  2347  4328  4272  4220  3944  4185  3975  4220  2979   \n",
      "354516         0   70  3878  4316  4564  4728  5445  5242  5489  4500  3836   \n",
      "354517         0   70  3878  4460  4400  4792  5445  5242  5489  4600  3836   \n",
      "...          ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "120332403      4   70   264   236   466   263   702  2844  3513  3340  3834   \n",
      "120409747      3   70   589   322   462   473  1077  1570  1836  1596  1835   \n",
      "120420734      3   70   760   713   939   991  1288  1654  1832  1780  1812   \n",
      "120464649      3   70   768   517   576   686  1084  1603  1857  2064  2022   \n",
      "120519538      3   70   442   329   530   438   949  1697  1891  1992  1924   \n",
      "\n",
      "            B11   B12   B8A  SCL   WVP  \n",
      "350913     2853  1803  2566    4  2728  \n",
      "354514     3758  2708  3884    9  2241  \n",
      "354515     3758  2708  3884    9  2241  \n",
      "354516     4589  2917  5310    9  2518  \n",
      "354517     4589  2917  5310    9  2518  \n",
      "...         ...   ...   ...  ...   ...  \n",
      "120332403  1826   835  3809    4  2664  \n",
      "120409747  1815  1329  1947    4  2520  \n",
      "120420734  1817  1621  1801    5  2423  \n",
      "120464649  2193  1752  1983    5  2584  \n",
      "120519538  1891  1408  2117    4  2613  \n",
      "\n",
      "[326391 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "bands_folder_path = 'resources/S2_17SLD_2019-09-15_masked_warped'\n",
    "\n",
    "# list of the paths of the 15 bands\n",
    "band_paths = [os.path.join(bands_folder_path, my_file)\n",
    "              for my_file in os.listdir(bands_folder_path)\n",
    "              if os.path.isfile(os.path.join(bands_folder_path, my_file))]\n",
    "\n",
    "bands_names = list()\n",
    "for band_number in range(len(band_paths)):\n",
    "    with rasterio.open(band_paths[band_number]) as band_dataset:\n",
    "        band_name = '{}'.format(band_paths[band_number].split('\\\\')[1].split('_')[2])\n",
    "        band_df = pd.DataFrame(band_dataset.read(1).ravel()).rename(columns = {0: band_name})\n",
    "        bands_names.append(band_name)\n",
    "        \n",
    "        # concatenation of the band with the dataset\n",
    "        reduced_label_band_df = pd.concat([reduced_label_band_df, band_df.reindex(reduced_label_band_df.index)], axis = 1)\n",
    "        print(reduced_label_band_df)\n",
    "        band_dataset.close()\n",
    "\n",
    "# I reset the index and store the previous one : if necessary we could unravel it to translate it into (x, y) coordinates\n",
    "final_dataset_df = reduced_label_band_df.reset_index().rename(columns = {'index': 'previous_index'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now reindex and add of a label mapping in order to improve readibility of the dataset. I used the [European Space Agency documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) to translate the titles of the TIF files provided into scientific features :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>previous_index</th>\n",
       "      <th>label</th>\n",
       "      <th>AOT</th>\n",
       "      <th>coastal_aerosol</th>\n",
       "      <th>blue</th>\n",
       "      <th>green</th>\n",
       "      <th>red</th>\n",
       "      <th>IR_705nm</th>\n",
       "      <th>IR_740nm</th>\n",
       "      <th>IR_783nm</th>\n",
       "      <th>IR_842nm</th>\n",
       "      <th>water_vapour</th>\n",
       "      <th>SWIR_1610nm</th>\n",
       "      <th>SWIR_2190nm</th>\n",
       "      <th>IR_865nm</th>\n",
       "      <th>SCL</th>\n",
       "      <th>WVP</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350913</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>360</td>\n",
       "      <td>391</td>\n",
       "      <td>640</td>\n",
       "      <td>735</td>\n",
       "      <td>1274</td>\n",
       "      <td>1954</td>\n",
       "      <td>2300</td>\n",
       "      <td>2374</td>\n",
       "      <td>2620</td>\n",
       "      <td>2853</td>\n",
       "      <td>1803</td>\n",
       "      <td>2566</td>\n",
       "      <td>4</td>\n",
       "      <td>2728</td>\n",
       "      <td>single tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>354514</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>2347</td>\n",
       "      <td>2302</td>\n",
       "      <td>2674</td>\n",
       "      <td>2778</td>\n",
       "      <td>3944</td>\n",
       "      <td>4185</td>\n",
       "      <td>3975</td>\n",
       "      <td>2784</td>\n",
       "      <td>2979</td>\n",
       "      <td>3758</td>\n",
       "      <td>2708</td>\n",
       "      <td>3884</td>\n",
       "      <td>9</td>\n",
       "      <td>2241</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>354515</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>2347</td>\n",
       "      <td>4328</td>\n",
       "      <td>4272</td>\n",
       "      <td>4220</td>\n",
       "      <td>3944</td>\n",
       "      <td>4185</td>\n",
       "      <td>3975</td>\n",
       "      <td>4220</td>\n",
       "      <td>2979</td>\n",
       "      <td>3758</td>\n",
       "      <td>2708</td>\n",
       "      <td>3884</td>\n",
       "      <td>9</td>\n",
       "      <td>2241</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>354516</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3878</td>\n",
       "      <td>4316</td>\n",
       "      <td>4564</td>\n",
       "      <td>4728</td>\n",
       "      <td>5445</td>\n",
       "      <td>5242</td>\n",
       "      <td>5489</td>\n",
       "      <td>4500</td>\n",
       "      <td>3836</td>\n",
       "      <td>4589</td>\n",
       "      <td>2917</td>\n",
       "      <td>5310</td>\n",
       "      <td>9</td>\n",
       "      <td>2518</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>354517</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3878</td>\n",
       "      <td>4460</td>\n",
       "      <td>4400</td>\n",
       "      <td>4792</td>\n",
       "      <td>5445</td>\n",
       "      <td>5242</td>\n",
       "      <td>5489</td>\n",
       "      <td>4600</td>\n",
       "      <td>3836</td>\n",
       "      <td>4589</td>\n",
       "      <td>2917</td>\n",
       "      <td>5310</td>\n",
       "      <td>9</td>\n",
       "      <td>2518</td>\n",
       "      <td>urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   previous_index label  AOT  coastal_aerosol  blue  green   red  IR_705nm  \\\n",
       "0          350913     3   69              360   391    640   735      1274   \n",
       "1          354514     0   70             2347  2302   2674  2778      3944   \n",
       "2          354515     0   70             2347  4328   4272  4220      3944   \n",
       "3          354516     0   70             3878  4316   4564  4728      5445   \n",
       "4          354517     0   70             3878  4460   4400  4792      5445   \n",
       "\n",
       "   IR_740nm  IR_783nm  IR_842nm  water_vapour  SWIR_1610nm  SWIR_2190nm  \\\n",
       "0      1954      2300      2374          2620         2853         1803   \n",
       "1      4185      3975      2784          2979         3758         2708   \n",
       "2      4185      3975      4220          2979         3758         2708   \n",
       "3      5242      5489      4500          3836         4589         2917   \n",
       "4      5242      5489      4600          3836         4589         2917   \n",
       "\n",
       "   IR_865nm  SCL   WVP        label  \n",
       "0      2566    4  2728  single tree  \n",
       "1      3884    9  2241        urban  \n",
       "2      3884    9  2241        urban  \n",
       "3      5310    9  2518        urban  \n",
       "4      5310    9  2518        urban  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# translation of the columns title with a mapping dictionnary\n",
    "bands_meaning = ['AOT',\n",
    "                 'coastal_aerosol',\n",
    "                 'blue',\n",
    "                 'green',\n",
    "                 'red',\n",
    "                 'IR_705nm',\n",
    "                 'IR_740nm',\n",
    "                 'IR_783nm',\n",
    "                 'IR_842nm',\n",
    "                 'water_vapour',\n",
    "                 'SWIR_1610nm',\n",
    "                 'SWIR_2190nm',\n",
    "                 'IR_865nm',\n",
    "                 'SCL',\n",
    "                 'WVP']\n",
    "bands_meaning_mapping = {bands_names[i]: bands_meaning[i] for i in range(len(bands_names))}\n",
    "final_dataset_df = final_dataset_df.rename(columns = bands_meaning_mapping)\n",
    "\n",
    "#insertion of the label_meaning column\n",
    "label_mapping = {0: 'urban', 1: 'treerow', 2: 'forest', 3: 'single tree', 4: 'agriculture', 5: 'grassland', 6: 'water'}\n",
    "label_meaning_df = final_dataset_df['label'].replace(label_mapping)\n",
    "\n",
    "pd.concat([final_dataset_df, label_meaning_df], axis = 1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Storing of the final dataset into a csv :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset_df.to_csv('final_dataset.csv',  index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHECKPOINT** : To this point, we don't need to execute the code above anymore : the dataset is definitly stored in the file 'final_dataset.csv'. However, if we restart the kernel, we will still need to execute the first bloc of imports before continuing to execute the Model Training part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Classifier Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the dataset into train and test data : 80% for the training data and 20% for the testing one.\n",
    "\n",
    "Furthermore, as long as we want to predict a class, we will use a Classifier model. It chose here to use a RandomForestClassifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset_df = pd.read_csv('final_dataset.csv')\n",
    "\n",
    "# split of the dataset\n",
    "prediction_target = final_dataset_df['label']\n",
    "X1 = final_dataset_df.drop(columns = ['label', 'previous_index'])\n",
    "train_data1, test_data1, train_prediction_target1, test_prediction_target1 = train_test_split(X1, prediction_target, train_size = 0.8, random_state = 1)\n",
    "\n",
    "# classifier training\n",
    "model1 = RandomForestClassifier(random_state = 1)\n",
    "model1.fit(train_data1, train_prediction_target1)\n",
    "predictions1 = model1.predict(test_data1)\n",
    "\n",
    "# evaluation of the classifier\n",
    "confusion_mtrx_1 = confusion_matrix(test_prediction_target1, predictions1)\n",
    "class_report_1 = classification_report(test_prediction_target1, predictions1, zero_division = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      3795\n",
      "           1       0.93      0.78      0.85      1390\n",
      "           2       0.99      1.00      0.99     30760\n",
      "           3       1.00      0.00      0.00        21\n",
      "           4       0.99      1.00      0.99     18896\n",
      "           5       0.98      0.93      0.95      1496\n",
      "           6       1.00      1.00      1.00      8921\n",
      "\n",
      "    accuracy                           0.99     65279\n",
      "   macro avg       0.98      0.81      0.83     65279\n",
      "weighted avg       0.99      0.99      0.99     65279\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# smooth plot of the confusion matrix\n",
    "label_mapping = {0: 'urban', 1: 'treerow', 2: 'forest', 3: 'single tree', 4: 'agriculture', 5: 'grassland', 6: 'water'}\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    test_prediction_target1.replace(label_mapping), \n",
    "    pd.DataFrame(predictions1).replace(label_mapping),\n",
    "    figsize = (10, 10),\n",
    "    title = 'Label confusion matrix',\n",
    "    x_tick_rotation = 45,\n",
    "    cmap = 'Greens')\n",
    "\n",
    "print(class_report_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already have satifying results of the model but we can do better. Let's improve the model ! We can squash the 3 RGB layers into a unique grayscale layer :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset_df = pd.read_csv('final_dataset.csv')\n",
    "\n",
    "# creation of the grayscale column\n",
    "# the weights are respectively for : red, green, blue\n",
    "rgb_weights = [0.2989, 0.5870, 0.1140]\n",
    "rgb_df = pd.concat([final_dataset_df['blue'], final_dataset_df['green'], final_dataset_df['red']], axis = 1)\n",
    "grayscale_df = rgb_df.dot(rgb_weights)\n",
    "grayscale_df = grayscale_df.map(np.round).astype(int)\n",
    "final_dataset_df['grayscale'] = grayscale_df\n",
    "\n",
    "# split of the dataset\n",
    "prediction_target = final_dataset_df['label']\n",
    "X2 = final_dataset_df.drop(columns = ['label', 'blue', 'green', 'red'])\n",
    "train_data2, test_data2, train_prediction_target2, test_prediction_target2 = train_test_split(X2, prediction_target, train_size = 0.8, random_state = 1)\n",
    "\n",
    "# classifier training\n",
    "model2 = RandomForestClassifier(random_state = 1)\n",
    "model2.fit(train_data2, train_prediction_target2)\n",
    "predictions2 = model2.predict(test_data2)\n",
    "\n",
    "# evaluation of the classifier :\n",
    "confusion_mtrx_2 = confusion_matrix(test_prediction_target2, predictions2)\n",
    "class_report_2 = classification_report(test_prediction_target2, predictions2, zero_division = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      3795\n",
      "           1       0.98      0.93      0.95      1390\n",
      "           2       1.00      1.00      1.00     30760\n",
      "           3       1.00      0.10      0.17        21\n",
      "           4       1.00      1.00      1.00     18896\n",
      "           5       0.99      0.98      0.99      1496\n",
      "           6       1.00      1.00      1.00      8921\n",
      "\n",
      "    accuracy                           1.00     65279\n",
      "   macro avg       0.99      0.86      0.87     65279\n",
      "weighted avg       1.00      1.00      1.00     65279\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# smooth plot of the confusion matrix\n",
    "label_mapping = {0: 'urban', 1: 'treerow', 2: 'forest', 3: 'single tree', 4: 'agriculture', 5: 'grassland', 6: 'water'}\n",
    "skplt.metrics.plot_confusion_matrix(\n",
    "    test_prediction_target2.replace(label_mapping), \n",
    "    pd.DataFrame(predictions2).replace(label_mapping),\n",
    "    figsize = (10, 10),\n",
    "    title = 'Label confusion matrix',\n",
    "    x_tick_rotation = 45,\n",
    "    cmap = 'Greens')\n",
    "\n",
    "print(class_report_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better ! We improved by respectively **10 points** and **4 points** the f1-score of the treerow label and the grassland label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Classifier limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest model is pretty bad at detecting single tree and tree row. An explanation to this behaviours is that the model should be able to **detect the nature of the direct neighbours of a pixel** in order to see if there are trees around or not. It would allow us to distinguish single tree and tree row from forest label.\n",
    "\n",
    "Furthermore, one thing we could do with a little more time is to take advantage of the fact that **a hot body emits IR**. I also know that the heat above a field is higher than the average. With this hypothesis, we could then improve our model by focusing on the different IR wavelengths features to improve the agriculture and grassland recognition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you had a nice reading !\n",
    "\n",
    "Thibault Latrace\n",
    "\n",
    "![Earth](resources/earth.jpg)"
   ]
  }
 ],
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