Remote_Sensing_Predictables_Classification_Without_Assets_V1_02_.ipynb

{
 "cells": [
  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Installs geemap package\n",
    "!pip install geemap\n",
    "!pip install ipygee\n",
    "!pip install geopandas\n",
    "!pip install js2py\n",
    "!pip install folium\n",
    "!pip install rasterio\n",
    "!pip install tslearn\n",
    "!pip install earthengine-api\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yf4U-DhHdLbO",
    "outputId": "2814c6c4-333e-4caf-fedc-3faf305123bd",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/hubert/Desktop\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p>To authorize access needed by Earth Engine, open the following\n",
       "        URL in a web browser and follow the instructions:</p>\n",
       "        <p><a href=https://accounts.google.com/o/oauth2/auth?client_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fearthengine+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&code_challenge=PT7GTNFi1LvXJ132cR5tcKBhF_sPqzcasjGEOC-FAm0&code_challenge_method=S256>https://accounts.google.com/o/oauth2/auth?client_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fearthengine+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&code_challenge=PT7GTNFi1LvXJ132cR5tcKBhF_sPqzcasjGEOC-FAm0&code_challenge_method=S256</a></p>\n",
       "        <p>The authorization workflow will generate a code, which you\n",
       "        should paste in the box below</p>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter verification code: 4/1AX4XfWgGcweSkRIHKcKrKEB23AssR7aSCsta-zd0eDq26hyZYE3SqnZRJ54\n",
      "\n",
      "Successfully saved authorization token.\n",
      "Random Forest:\n",
      "   Accuracy  Precision    Recall  Specificity  F1-score  Kappa statistic\n",
      "0  0.909761   0.843109  0.754593     0.957131  0.796399         0.738662\n",
      "\n",
      "Covered Areas(ha):\n",
      "{'Cropped Area': 45900, 'Non Cropped Area': 217900}\n",
      "\n",
      "Prediction between: 15 FEB - 27 MAR\n",
      "Covered Areas(ha):\n",
      "{'Cropped Area': 22100, 'Non Cropped Area': 241700}\n",
      "\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/70caa7ddd7124de4e9448c431e0e8a7b-b49b5393003969dd251d4ccc028dbdd3:getPixels\n",
      "Please wait ...\n",
      "Data downloaded to /home/hubert/Desktop/Results/crop_noncrop_classification_2021-02-15_to_2021-03-27_12132021_1040.tif\n",
      "Prediction between: 01 APR - 30 MAY\n",
      "Covered Areas(ha):\n",
      "{'Cropped Area': 30900, 'Non Cropped Area': 233000}\n",
      "\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/6dbf4a9ae888bda79b6f6293073e5c4f-4a3343375fd3fde8917a7f3215522f42:getPixels\n",
      "Please wait ...\n",
      "Data downloaded to /home/hubert/Desktop/Results/crop_noncrop_classification_2021-04-01_to_2021-05-30_12132021_1041.tif\n",
      "Prediction between: FEB - June\n",
      "Covered Areas(ha):\n",
      "{'Cropped Area': 33400, 'Non Cropped Area': 230400}\n",
      "\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/2e456ac72749cee4e92ebcfd100cff2d-5d36caafb76422918d0c638274e5ebc8:getPixels\n",
      "Please wait ...\n",
      "Data downloaded to /home/hubert/Desktop/Results/crop_noncrop_classification_2021-02-01_to_2021-06-30_12132021_1042.tif\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Created on Nov 2 at Manobi Africa Dakar 16:34:42 2021\n",
    "\n",
    "@authors: Pierre C. Traore - ICRISAT/ Manobi Africa\n",
    "          Steven Ndung'u - ICRISAT/ Manobi Africa\n",
    "          Hubert Kanyamahanga - ICRISAT/ Manobi Africa\n",
    "          Glorie Wowo - - ICRISAT/ Manobi Africa\n",
    "\"\"\"\n",
    "################################################################################\n",
    "########################### Introduction  ######################################\n",
    "################################################################################\n",
    "\n",
    "'''\n",
    "This notebook shows you how to perform supervised classification.The Classifier \n",
    "package handles supervised classification using Earth Engine resources. \n",
    "The general workflow for classification is:\n",
    "'''\n",
    "#####0. Environment Setup\n",
    "#####1. Collect training data. Assemble features which have a property that \n",
    "#######stores the known class label and properties storing numeric values for the predictors.\n",
    "#####2. Instantiate a classifier. Set its parameters if necessary.\n",
    "#####3. Train the classifier using the training data.\n",
    "#####4. Classify an image or feature collection.\n",
    "#####5. Estimate classification errors with independent validation data.\n",
    "#####6. Generate raster products from of aoi given date windows\n",
    "#####7. Generate estimated pixel areas per class classified\n",
    "\n",
    "################################################################################\n",
    "####################### Install and load packages ##############################\n",
    "################################################################################\n",
    "\n",
    "''' if you are working with Colab, this packages will require to be installed every time the \n",
    "script is run. If runnning from a local machine this can be commented out/deleted\n",
    "'''\n",
    "# # Installs geemap package\n",
    "# !pip install geemap\n",
    "# !pip install ipygee\n",
    "# !pip install geopandas\n",
    "# !pip install js2py\n",
    "# !pip install folium\n",
    "# !pip install rasterio\n",
    "# !pip install tslearn\n",
    "# !pip install earthengine-api\n",
    "\n",
    "# Import os packages\n",
    "import os, ee, json, subprocess\n",
    "import ipygee as ui\n",
    "import geemap\n",
    "from os import path as op\n",
    "from datetime import datetime\n",
    "\n",
    "#Pandas modules to interact with spatial data\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from geopandas import GeoDataFrame\n",
    "from shapely.geometry import Point,Polygon\n",
    "\n",
    "#import numpy\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "\n",
    "################################################################################\n",
    "################## Authenticate/Setup GEE, GDRIVE, WP #########################\n",
    "################################################################################\n",
    "%cd '/home/hubert/Desktop'\n",
    "output_path = \"/home/hubert/Desktop/Results\"\n",
    "\n",
    "# Make sure that folder exists\n",
    "if (not op.isdir(output_path)):\n",
    "  os.makedirs(output_path)\n",
    "# Login into GEE\n",
    "ee.Authenticate()\n",
    "ee.Initialize()\n",
    "\n",
    "#set up your working paths\n",
    "\n",
    "# Change the file paths to match the file locations in your Gee project.\n",
    "\n",
    "study_site_path = \"projects/ee-hubertkanye/assets/UpperLowerDeltaMerged\"\n",
    "crop_noncrop = \"projects/ee-hubertkanye/assets/crop_noncrop_new\"\n",
    "\n",
    "dpath_root = os.path.dirname(os.path.join(os.path.realpath('__file__')))\n",
    "\n",
    "\n",
    "# def shp2geojson(dpath, fname):\n",
    "#     gpd.read_file(os.path.join(dpath, f'{fname}.shp')).to_file(os.path.join(dpath, f'{fname}.geojson'), driver='GeoJSON')\n",
    "\n",
    "# shp2geojson(os.path.join(dpath_root, 'crop-noncrop-2021.12.03', 'Studysite'), 'UpperLowerDeltaMerged')\n",
    "# shp2geojson(os.path.join(dpath_root, 'crop-noncrop-2021.12.03', 'crop_noncrop_updated'), 'crop_noncrop_training_set_representative_28112021')\n",
    "\n",
    "# with open(os.path.join(dpath_root, 'crop-noncrop-2021.12.03', 'Studysite', 'UpperLowerDeltaMerged.geojson')) as f:\n",
    "#     geojson_studysite = json.load(f)\n",
    "\n",
    "# with open(os.path.join(dpath_root, 'crop-noncrop-2021.12.03', 'crop_noncrop_updated', 'crop_noncrop_training_set_representative_28112021.geojson')) as f:\n",
    "#     geojson_crop_noncrop = json.load(f)\n",
    "\n",
    "\n",
    "# studysite, crop_noncrop = load_data(geojson_studysite, geojson_crop_noncrop)\n",
    "\n",
    "\n",
    "# Select the start and end date of the time series of interest:\n",
    "start_date = \"2020-02-01\"\n",
    "end_date = \"2020-06-30\"\n",
    "# Output directory\n",
    "out_dir = os.path.join(os.path.expanduser(\"~\"), output_path)\n",
    "\n",
    "################################################################################\n",
    "####################### Model Building & Preprocessing #########################\n",
    "################################################################################\n",
    "\n",
    "# Create an interactive map\n",
    "#Map = geemap.Map()\n",
    "\n",
    "# Function to mask clouds S2\n",
    "def mask_cloud_and_shadows(image):\n",
    "    quality_band = image.select(\"QA60\")\n",
    "    cloudmask = 1 << 10\n",
    "    cirrusmask = 1 << 11\n",
    "    mask = quality_band.bitwiseAnd(cloudmask).eq(0) and (\n",
    "        quality_band.bitwiseAnd(cirrusmask).eq(0)\n",
    "    )\n",
    "    return image.updateMask(mask).divide(10000)\n",
    "\n",
    "\n",
    "# Function to calculate vegetative indices layers, additional feature\n",
    "#to enrich the classification product.\n",
    "\n",
    "def add_indices(image):\n",
    "    #selected indices: other veg indices of interest can be added\n",
    "    ndbi = image.normalizedDifference([\"B11\", \"B8\"]).rename([\"ndbi\"])\n",
    "    ndvi = image.normalizedDifference([\"B8\", \"B4\"]).rename([\"ndvi\"])\n",
    "    mndwi = image.normalizedDifference([\"B3\", \"B11\"]).rename([\"mndwi\"])\n",
    "    bsi = image.expression(\n",
    "        \"(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) \",\n",
    "        {\n",
    "            \"X\": image.select(\"B11\"),  # swir1\n",
    "            \"Y\": image.select(\"B4\"),  # red\n",
    "            \"A\": image.select(\"B8\"),  # nir\n",
    "            \"B\": image.select(\"B2\"),  # blue\n",
    "        },\n",
    "    ).rename(\"bsi\")\n",
    "    return image.addBands(ndbi).addBands(ndvi).addBands(mndwi).addBands(bsi)\n",
    "\n",
    "def load_data(study_site_path, crop_noncrop):\n",
    "    # 1 Load SENTINEL-2 IMAGE COLLECTION\n",
    "    sent2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "    # 2 Load file that covers the Upper delta region of the SRV\n",
    "    studysite = ee.FeatureCollection(study_site_path)\n",
    "    # 3 Load our training datasets that containts crop and non crop geometry\n",
    "    crop_noncrop = ee.FeatureCollection(crop_noncrop)\n",
    "    return studysite, crop_noncrop\n",
    "\n",
    "\n",
    "def get_composite(start_date, end_date, studysite):\n",
    "    \"\"\"Returns the composite\"\"\"\n",
    "    # 4 Add our area of interest to the Map\n",
    "    #Map.addLayer(studysite.geometry(), {}, \"Area of Interest\")\n",
    "\n",
    "    # We will now search for Sentinel 2 imagery, a multispectral satellite\n",
    "    # with ~10m resolution and repeat coverage every 5 days.\n",
    "    # Filters will include selecting bands, a date range, and only imagery within a defined Area of Interest (AOI).\n",
    "\n",
    "    filtered = (\n",
    "        ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "        .filterDate(start_date, end_date)\n",
    "        .filterMetadata(\"CLOUDY_PIXEL_PERCENTAGE\", \"less_than\", 20)\n",
    "        .map(mask_cloud_and_shadows)\n",
    "        .filter(ee.Filter.bounds(studysite.geometry()))\n",
    "    ).select(\"B.*\", \"SCL\")\n",
    "    # Input imagery is a cloud-free Sentinel_2 composite.\n",
    "    composite = filtered.median().clip(studysite)\n",
    "    # Append the vegetative indices layers to the composite\n",
    "    composite = add_indices(composite)\n",
    "    #Map.addLayer(composite, {\"bands\": [\"B4\", \"B3\", \"B2\"]}, \"filtered_composite\")\n",
    "   # Map.addLayer(crop_noncrop.geometry())\n",
    "    return composite\n",
    "\n",
    "\n",
    "def get_class_labels(crop_noncrop):\n",
    "    \"\"\"Returns the class labels\"\"\"\n",
    "\n",
    "    crop = crop_noncrop.filter(ee.Filter.eq(\"landcover\", 1))\n",
    "    noncrop = crop_noncrop.filter(ee.Filter.eq(\"landcover\", 2))\n",
    "    # Combine the manually trained data of the crops into a reference dataset*/\n",
    "    class_labels = crop.merge(noncrop)\n",
    "    # Add our classes to the Map\n",
    "    #Map.addLayer(class_labels)\n",
    "    return class_labels\n",
    "\n",
    "\n",
    "def train_test_split(composite, class_labels):\n",
    "    \"\"\"Returns the both the training and testing partitions\"\"\"\n",
    "\n",
    "    # Overlay the point on the image to get training data.\n",
    "    training = composite.sampleRegions(\n",
    "        **{\"collection\": class_labels, \"properties\": [\"landcover\"], \"scale\": 70}\n",
    "    )\n",
    "    # random uniforms to the training dataset.\n",
    "    withRandom = training.randomColumn()\n",
    "\n",
    "    # We want to reserve some of the data for testing, to avoid overfitting the model.\n",
    "    split = 0.7  # Roughly 70% training, 30% testing.\n",
    "    trainingPartition = withRandom.filter(ee.Filter.lt(\"random\", split))\n",
    "    testingPartition = withRandom.filter(ee.Filter.gte(\"random\", split))\n",
    "    return trainingPartition, testingPartition\n",
    "\n",
    "# Define some variables to be used for model training and validation\n",
    "studysite, crop_noncrop = load_data(study_site_path, crop_noncrop)\n",
    "composite = get_composite(start_date, end_date, studysite)\n",
    "class_labels = get_class_labels(crop_noncrop)\n",
    "train_data, test_data = train_test_split(composite, get_class_labels(crop_noncrop))\n",
    "\n",
    "\n",
    "def train_the_classifier(params, model, train_data):\n",
    "    \"\"\"Returns the classifier model\"\"\"\n",
    "\n",
    "    label = \"landcover\"\n",
    "    # Training the classifier: model = smileRandomForest\n",
    "    classifier = (\n",
    "        model(**params)\n",
    "        .setOutputMode(\"CLASSIFICATION\")\n",
    "        .train(train_data, label, composite.bandNames())\n",
    "    )\n",
    "    # Create an RF classifier with custom parameters\n",
    "    return classifier\n",
    "\n",
    "\n",
    "def perform_classification(composite):\n",
    "    # Classify the image.\n",
    "    classified = composite.classify(\n",
    "        train_the_classifier(\n",
    "            rf_init_params, ee.Classifier.smileRandomForest, train_data\n",
    "        )\n",
    "    )\n",
    "    sld_intervals_crop = (\n",
    "        \"<RasterSymbolizer>\"\n",
    "        + '<ColorMap type=\"intervals\" extended=\"false\">'\n",
    "        + '<ColorMapEntry color=\"#10d22c\" quantity=\"1\" label=\"Crop\"/>'\n",
    "        + '<ColorMapEntry color=\"#000000\" quantity=\"2\" label=\"Non Crop\"/>'\n",
    "        + \"</ColorMap>\"\n",
    "        + \"</RasterSymbolizer>\"\n",
    "    )\n",
    "    # Add the classified image to the Map\n",
    "   # Map.addLayer(classified.sldStyle(sld_intervals_crop), {}, \"RF Classified Layer\")\n",
    "    # Map.addLayer(classified, {\"min\": 0, max:3, \"palette\": ['#37d615', '#3223d6']}, 'RF_classified')\n",
    "    return classified\n",
    "\n",
    "# Set initial params for RF classifier\n",
    "rf_init_params = {\n",
    "    \"numberOfTrees\": 150,  # the number of individual decision tree models\n",
    "    \"variablesPerSplit\": None,  # the number of features to use per split\n",
    "    \"minLeafPopulation\": 1,  # smallest sample size possible per leaf\n",
    "    \"bagFraction\": 0.5,  # fraction of data to include for each individual tree model\n",
    "    \"maxNodes\": None,  # max number of leafs/nodes per tree\n",
    "    \"seed\": 34,\n",
    "}  # random seed for \"random\" choices like sampling. Setting this allows others to \n",
    "   # reproduce your exact results even with stochastic parameters\n",
    "\n",
    "\n",
    "# Define the classifier and the classified \n",
    "classifier = train_the_classifier(\n",
    "    rf_init_params, ee.Classifier.smileRandomForest, train_data\n",
    ")\n",
    "\n",
    "classified = perform_classification(composite)\n",
    "\n",
    "################################################################################\n",
    "############################ Model Evaluation ##################################\n",
    "################################################################################\n",
    "\n",
    "\n",
    "def test_the_classifier(classifier, test_data):\n",
    "    \"\"\"Returns the confusion matrix\"\"\"\n",
    "\n",
    "    # test_data:  testingPartition\n",
    "    test = test_data.classify(classifier)\n",
    "    # Get a confusion matrix representing expected accuracy.\n",
    "    rf_confusionMatrix = test.errorMatrix(\"landcover\", \"classification\")\n",
    "    return rf_confusionMatrix\n",
    "\n",
    "\n",
    "rf_confusionMatrix = test_the_classifier(classifier, test_data)\n",
    "\n",
    "# 5. Accuracy Assessment\n",
    "\n",
    "# More statistics\n",
    "headers = [\n",
    "    \"Accuracy\",\n",
    "    \"Precision\",\n",
    "    \"Recall\",\n",
    "    \"Specificity\",\n",
    "    \"F1-score\",\n",
    "    \"Kappa statistic\",\n",
    "]\n",
    "\n",
    "metrics = []\n",
    "\n",
    "def compute_metrics(conf_matr):\n",
    "    \"\"\"Returns a list of six computed metrics\"\"\"\n",
    "\n",
    "    y = np.array(\n",
    "        [\n",
    "            [conf_matr.getInfo()[1][1], conf_matr.getInfo()[1][2]],\n",
    "            [conf_matr.getInfo()[2][1], conf_matr.getInfo()[2][2]],\n",
    "        ]\n",
    "    )\n",
    "    cm = np.asmatrix(y)\n",
    "    accurracy = conf_matr.accuracy().getInfo()\n",
    "    metrics.append(accurracy)\n",
    "    precision = cm[0, 0] / (cm[0, 0] + cm[1, 0])\n",
    "    metrics.append(precision)\n",
    "    recall = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n",
    "    metrics.append(recall)\n",
    "    specificity = cm[1, 1] / (cm[1, 0] + cm[1, 1])\n",
    "    metrics.append(specificity)\n",
    "    f1 = 2 * (precision * recall) / (precision + recall)\n",
    "    metrics.append(f1)\n",
    "    kappa = conf_matr.kappa().getInfo()\n",
    "    metrics.append(kappa)\n",
    "    return metrics\n",
    "\n",
    "\n",
    "data = compute_metrics(test_the_classifier(classifier, test_data))\n",
    "rf_acc_ass = pd.DataFrame(data=[data], columns=headers)\n",
    "print(f\"Random Forest:\\n{rf_acc_ass.head()}\\n\")\n",
    "\n",
    "################################################################################\n",
    "######################### Model Outputs/Exports ################################\n",
    "################################################################################\n",
    "\n",
    "\n",
    "def export_accurracy_assessment(out_dir, data, header):\n",
    "    \"\"\"Exports the metrics as a csv file to the provided output path\"\"\"\n",
    "    accurracy_assessment = os.path.join(out_dir, \"accurracy_assessment.json\")\n",
    "    data = dict(zip(header, data))\n",
    "    with open(accurracy_assessment, 'w') as outfile:\n",
    "        json.dump(data, outfile)\n",
    "\n",
    "def export_classified_tiff(classified, studysite,start_date,end_date):\n",
    "    \"\"\"Export the image, specifying scale and region.\"\"\"\n",
    "    # Warning, This specific export takes at least 1 hour, and 700+ MB\n",
    "    # GEE folder does not use a path, it uses a unique name to find the export location\n",
    "\n",
    "    now = datetime.now()  # current date and time\n",
    "    time =  now.strftime(\"%m%d%Y_%H%M\")\n",
    "    # We will save it to Google Drive for later reuse\n",
    "    raster_name = op.join(out_dir, \"crop_noncrop_classification_{}_to_{}_{}\".format(start_date,end_date,str(time)))\n",
    "    task = ee.batch.Export.image.toDrive(\n",
    "        **{\n",
    "            \"image\": classified,\n",
    "            \"description\": \"crop_noncrop_classification_{}_to_{}_{}\".format(start_date,end_date,str(time)),\n",
    "            \"folder\": op.basename(output_path),\n",
    "            \"scale\": 10,\n",
    "            \"fileNamePrefix\": raster_name.split('/')[-1],\n",
    "            \"region\": studysite.geometry(),\n",
    "            \"fileFormat\": \"GeoTIFF\",\n",
    "            \"formatOptions\": {\"cloudOptimized\": \"true\"},\n",
    "            \"maxPixels\": 1e12,\n",
    "        }\n",
    "    )\n",
    "    # This task will run in the background even if you close this notebook.\n",
    "    # You can also check on the status of the task through the Javascript GEE interface\n",
    "    # https://code.earthengine.google.com\n",
    "    return task.start()\n",
    "\n",
    "\n",
    "\n",
    "def download_classified_tiff(classified, studysite,start_date,end_date):\n",
    "    \"\"\"Export and download the image, specifying scale and region.\"\"\"\n",
    "    now = datetime.now()  # current date and time\n",
    "    time =  now.strftime(\"%m%d%Y_%H%M\")\n",
    "    # The image will be saved in your local folder\n",
    "    filename = op.join(out_dir, \"crop_noncrop_classification_{}_to_{}_{}{}\".format(start_date,end_date,str(time), '.tif'))\n",
    "    geemap.ee_export_image(classified, filename=filename, scale=90, region=studysite.geometry(), file_per_band=False)\n",
    "\n",
    "\n",
    "################################################################################\n",
    "################### Area calculations (Pixel Based) ############################\n",
    "################################################################################\n",
    "\n",
    "# Class Area calculation\n",
    "\n",
    "def get_item(item):\n",
    "  areaDict = ee.Dictionary(item)\n",
    "  classNumber = ee.Number(areaDict.get('classification')).format()\n",
    "  # The result will be in square meters, this converts them into square kilometers \n",
    "  area = ee.Number(areaDict.get('sum')).divide(1e6).round()\n",
    "  return ee.List([classNumber, area])\n",
    "\n",
    "def classAreaLists(class_areas):\n",
    "  return class_areas.map(get_item) \n",
    "\n",
    "def export_class_area(classified):\n",
    "  areaImage = ee.Image.pixelArea().addBands(classified)\n",
    "  areas = areaImage.reduceRegion(**{ \n",
    "      \"reducer\": ee.Reducer.sum().group(**{\n",
    "              \"groupField\": 1,\n",
    "              \"groupName\": \"classification\",\n",
    "      }),\n",
    "      \"geometry\": studysite.geometry(),\n",
    "      \"scale\": 10, \n",
    "      \"maxPixels\": 1e9\n",
    "      })\n",
    "  class_areas = ee.List(areas.get(\"groups\"))\n",
    "  # Flattens said dictionary so it is readable for us\n",
    "  result = ee.Dictionary(classAreaLists(class_areas).flatten()) \n",
    "  now = datetime.now()  # current date and time\n",
    "  time =  now.strftime(\"%m%d%Y_%H%M\")\n",
    "  class_area = os.path.join(out_dir, \"class_area_{}.json\".format(time))\n",
    "  serialized_data = result.getInfo()\n",
    "  now = datetime.now()  # current date and time\n",
    "  time =  now.strftime(\"%m%d%Y%H%M%S\")\n",
    "  data = {}\n",
    "  data[\"Cropped Area\"] = serialized_data[\"1\"] * 100 #ha\n",
    "  data[\"Non Cropped Area\"] = serialized_data[\"2\"]  * 100 #ha\n",
    "  print(f\"Covered Areas(ha):\\n{data}\\n\")\n",
    "  with open(class_area, 'w') as outfile:\n",
    "      json.dump(data, outfile)\n",
    "  return serialized_data\n",
    "\n",
    "export_class_area(classified)\n",
    "# Call the functions\n",
    "\n",
    "export_accurracy_assessment(out_dir, data, headers)\n",
    "#export_class_area(classified)\n",
    "#export_classified_tiff(classified, studysite,start_date,end_date)\n",
    "\n",
    "\n",
    "################################################################################\n",
    "################################# Predictions ##################################\n",
    "################################################################################\n",
    "\n",
    "'''\n",
    "Here, we want to generate a new product provided new windows for the following year. \n",
    "The model above has been trained using images sampled from 2020, we now can use the \n",
    "model to predict outputs  for 2021. \n",
    "'''\n",
    "# Create a new window\n",
    "\n",
    "################################################################################\n",
    "############################# 2021 Prediction 1 ################################\n",
    "################################################################################\n",
    "\n",
    "start_date_new = \"2021-02-15\"\n",
    "end_date_new = \"2021-03-27\"\n",
    "pred_comp = get_composite(start_date_new, end_date_new, studysite)\n",
    "print('Prediction between: 15 FEB - 27 MAR')\n",
    "pred_comp = perform_classification(pred_comp)\n",
    "export_class_area(pred_comp)\n",
    "export_classified_tiff(pred_comp, studysite,start_date_new, end_date_new)\n",
    "download_classified_tiff(pred_comp, studysite,start_date_new, end_date_new)\n",
    "\n",
    "################################################################################\n",
    "############################# 2021 Prediction 2 ################################\n",
    "################################################################################\n",
    "\n",
    "start_date_new = \"2021-04-01\"\n",
    "end_date_new = \"2021-05-30\"\n",
    "pred_comp1 = get_composite(start_date_new, end_date_new, studysite)\n",
    "print('Prediction between: 01 APR - 30 MAY')\n",
    "pred_comp1 = perform_classification(pred_comp1)\n",
    "export_class_area(pred_comp1)\n",
    "export_classified_tiff(pred_comp1, studysite,start_date_new, end_date_new)\n",
    "download_classified_tiff(pred_comp1, studysite,start_date_new, end_date_new)\n",
    "\n",
    "################################################################################\n",
    "############################# 2021 Prediction 3 ################################\n",
    "################################################################################\n",
    "\n",
    "start_date_new = \"2021-02-01\"\n",
    "end_date_new = \"2021-06-30\"\n",
    "pred_comp1 = get_composite(start_date_new, end_date_new, studysite)\n",
    "print('Prediction between: FEB - June')\n",
    "pred_comp1 = perform_classification(pred_comp1)\n",
    "export_class_area(pred_comp1)\n",
    "export_classified_tiff(pred_comp1, studysite,start_date_new, end_date_new)\n",
    "download_classified_tiff(pred_comp1, studysite,start_date_new, end_date_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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