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FATHOM updates
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bpstewar committed Apr 26, 2024
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298 changes: 298 additions & 0 deletions notebooks/BUILT_compare_buildings_wsf.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"import sys, os\n",
"import rasterio\n",
"\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"import numpy as np\n",
"\n",
"from shapely.wkt import loads\n",
"\n",
"sys.path.insert(0, \"../src\")\n",
"\n",
"import GOSTrocks.rasterMisc as rMisc\n",
"import GOSTrocks.dataMisc as dMisc\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\WB411133\\AppData\\Local\\Temp\\ipykernel_25180\\3039392467.py:14: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
" world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n"
]
}
],
"source": [
"# Local/input files\n",
"iso3 = 'KHM'\n",
"out_folder = \"c:/WBG/Work/KHM_Energy/data\"\n",
"wsf_file = os.path.join(out_folder, \"WSF\", \"wsf.tif\")\n",
"ghsl_file = os.path.join(out_folder, \"GHSL\", \"ghsl.tif\")\n",
"overture_buildings = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29.csv\")\n",
"overture_raster = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29.tif\")\n",
"overture_raster_points = os.path.join(out_folder, \"overture\", \"overture_download_2024_03_29_points.tif\")\n",
"for file in [wsf_file, ghsl_file]:\n",
" if not os.path.exists(os.path.dirname(file)):\n",
" os.makedirs(os.path.dirname(file))\n",
"\n",
"# get country extent from geopandas\n",
"world_filepath = gpd.datasets.get_path('naturalearth_lowres')\n",
"world = gpd.read_file(world_filepath)\n",
"country = world[world.iso_a3 == iso3]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FUBAR\n"
]
}
],
"source": [
"\"\"\" Not working with World Bank Firewall\n",
"# Download the WSF data\n",
"if not os.path.exists(wsf_file):\n",
" print(\"Downloading WSF data\")\n",
" wsf_data, wsf_profile = dMisc.download_WSF(country, out_file = wsf_file)\n",
"\n",
"wsf_r = rasterio.open(wsf_file)\n",
"\"\"\"\n",
"print(\"FUBAR\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Clip GHSL using local files\n",
"local_version = r\"J:\\Data\\GLOBAL\\GHSL\\Built\\GHS_BUILT_S_E2020_GLOBE_R2023A_54009_100_V1_0.tif\"\n",
"if not os.path.exists(ghsl_file):\n",
" ghsl_raster = rasterio.open(local_version)\n",
" data, profile = rMisc.clipRaster(ghsl_raster, country)\n",
" with rasterio.open(ghsl_file, 'w', **profile) as dst:\n",
" dst.write(data)\n",
"ghsl_r = rasterio.open(ghsl_file)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>height</th>\n",
" <th>wkt</th>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
" <td>POLYGON ((105.5873344 12.3655821, 105.5873972 ...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((103.0758412 13.2660819, 103.0758304 ...</td>\n",
" <td>POLYGON ((103.07584 13.26608, 103.07583 13.266...</td>\n",
" </tr>\n",
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],
"text/plain": [
" class height wkt \\\n",
"0 NaN NaN POLYGON ((103.3991092 13.6076154, 103.3991148 ... \n",
"1 NaN NaN POLYGON ((103.819971 13.2832912, 103.8199974 1... \n",
"2 NaN NaN POLYGON ((103.8152555 13.2890315, 103.8151626 ... \n",
"3 NaN NaN POLYGON ((105.5873344 12.3655821, 105.5873972 ... \n",
"4 NaN NaN POLYGON ((103.0758412 13.2660819, 103.0758304 ... \n",
"\n",
" geometry \n",
"0 POLYGON ((103.39911 13.60762, 103.39911 13.607... \n",
"1 POLYGON ((103.81997 13.28329, 103.82000 13.283... \n",
"2 POLYGON ((103.81526 13.28903, 103.81516 13.289... \n",
"3 POLYGON ((105.58733 12.36558, 105.58740 12.365... \n",
"4 POLYGON ((103.07584 13.26608, 103.07583 13.266... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read in and process Overture buildings\n",
"ob = pd.read_csv(overture_buildings)\n",
"ob_geoms = ob['wkt'].apply(loads)\n",
"inB = gpd.GeoDataFrame(ob, geometry=ob_geoms, crs=4326)\n",
"inB.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# attempt to rasterrize the buildings as polygons\n",
"if not os.path.exists(overture_raster):\n",
" rasterized_buildings = rMisc.rasterizeDataFrame(inB, templateRaster=ghsl_file, mergeAlg=\"ADD\", re_proj=True, nodata=0.)\n",
" with rasterio.open(overture_raster, 'w', **rasterized_buildings['meta']) as dst:\n",
" dst.write_band(1, rasterized_buildings['vals'])\n",
"overture_r = rasterio.open(overture_raster)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# attempt to rasterrize the buildings as points\n",
"if not os.path.exists(overture_raster_points):\n",
" inB_points = inB.copy()\n",
" inB_points['geometry'] = inB_points['geometry'].centroid\n",
" rasterized_buildings = rMisc.rasterizeDataFrame(inB_points, templateRaster=ghsl_file, mergeAlg=\"ADD\", re_proj=True, nodata=0.)\n",
" with rasterio.open(overture_raster_points, 'w', **rasterized_buildings['meta']) as dst:\n",
" dst.write_band(1, rasterized_buildings['vals'])\n",
"overture_r_points = rasterio.open(overture_raster_points) "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Compare rasterized buildings with built area dataset\n",
"### Open both datasets and threshold them to get built area data\n",
"o_thresh = 1\n",
"ghsl_thresh = 3000\n",
"\n",
"o_data = overture_r_points.read(1)\n",
"o_data = (o_data > o_thresh).astype('uint8')\n",
"\n",
"ghsl_data = ghsl_r.read(1)\n",
"ghsl_data = (ghsl_data > ghsl_thresh).astype('uint8') * 10\n",
"\n",
"combo_data = o_data + ghsl_data\n",
"\n",
"# Write out the combined data\n",
"out_file = os.path.join(out_folder, \"overture_vs_ghsl.tif\")\n",
"if not os.path.exists(out_file):\n",
" meta = overture_r_points.meta.copy()\n",
" meta.update(dtype=rasterio.uint8, nodata=0)\n",
" with rasterio.open(out_file, 'w', **meta) as out_raster:\n",
" out_raster.write_band(1, combo_data)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "urban_test",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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