features_extraction_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99a6c49e",
   "metadata": {},
   "source": [
    "### Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed192eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\geopandas\\_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import rasterio\n",
    "import rasterio.mask\n",
    "from matplotlib import colors, cm\n",
    "from osgeo import gdal\n",
    "import os\n",
    "from rasterio.plot import show\n",
    "from rasterio.plot import show_hist\n",
    "from numpy import ma\n",
    "# import richdem as rd\n",
    "from pyspatialml import Raster\n",
    "from tqdm import tqdm\n",
    "import rioxarray as rxr\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "508580ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\DELL\\\\Downloads\\\\geoailandslide'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42fbfbf",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d5ed045",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load geospatial datasets\n",
    "\n",
    "train_data = gpd.read_file(\"./datasets/Train.gpkg\",geometry='geometry')\n",
    "test_data = gpd.read_file(\"./datasets/Test.gpkg\",geometry='geometry')\n",
    "val_data = gpd.read_file(\"./datasets/valtellina.gpkg\",geometry='geometry')\n",
    "road_network = gpd.read_file(\"./datasets/road_network.gpkg\",geometry='geometry')\n",
    "river_network = gpd.read_file(\"./datasets/river_network.gpkg\",geometry='geometry')\n",
    "fault_zones = gpd.read_file(\"./datasets/geological_faults.gpkg\",geometry='geometry')\n",
    "land_use = gpd.read_file(\"./datasets/land_use_land_cover.gpkg\",geometry='geometry')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c737809",
   "metadata": {},
   "source": [
    "### Visualizing the map for the train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2da545e",
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 1.39 GiB for an array with shape (1, 14813, 25274) and data type float32",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_7292\\2631824894.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mDTM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\".\\datasets\\dtm.tif\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDTM\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'greys'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Target\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'train_map.png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tight'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\rasterio\\plot.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(source, with_bounds, contour, contour_label_kws, ax, title, transform, adjust, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m             \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmasked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mrasterio\\_io.pyx\u001b[0m in \u001b[0;36mrasterio._io.DatasetReaderBase.read\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 1.39 GiB for an array with shape (1, 14813, 25274) and data type float32"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "DTM = rasterio.open(\".\\datasets\\dtm.tif\")\n",
    "show(DTM,ax = ax, cmap = 'greys')\n",
    "train_data.plot(column=\"Target\", ax=ax)\n",
    "plt.savefig(r'train_map.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba04b630",
   "metadata": {},
   "outputs": [],
   "source": [
    "dem = gdal.Open(\".\\datasets\\dtm.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffaa47fa",
   "metadata": {},
   "source": [
    "### Extracting Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d12daa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "TPI = gdal.DEMProcessing(\"TPI.tif\", dem, 'TPI', computeEdges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27e6ec06",
   "metadata": {},
   "outputs": [],
   "source": [
    "TPI = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f807d4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# src = rasterio.open(\"TPI.tif\")\n",
    "# fig, ax = plt.subplots(figsize=(20, 10))\n",
    "# ax.set_title(\"Topographic Position Index\", fontsize= 20)\n",
    "# cmap = plt.get_cmap('terrain')\n",
    "# data = src.read()\n",
    "\n",
    "# data_ma = np.ma.masked_where(data == -9999 ,\n",
    "#                         data,\n",
    "#                          copy=True)\n",
    "# show(data_ma, transform=src.transform, ax=ax, cmap=cmap, vmin=-0.05, vmax=0.05)\n",
    "# fig.colorbar(cm.ScalarMappable(norm=colors.Normalize(vmin=-0.05, vmax=0.05), cmap=cmap), ax=ax)\n",
    "# plt.savefig(r'TPI.png', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}