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@ian-r-rose
Last active December 6, 2023 22:16
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Workflow demonstrating how to remove known bad footprints
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{
"cells": [
{
"cell_type": "markdown",
"id": "1f728179-efd0-46d3-b3c4-8dc122da18e7",
"metadata": {},
"source": [
"# Removing bad footprints\n",
"\n",
"Here we demonstrate how to remove known bad footprints from a geodataframe."
]
},
{
"cell_type": "markdown",
"id": "eb513856-b2fe-44be-9968-7553d835e1bb",
"metadata": {},
"source": [
"## Download Data \n",
"\n",
"In this case we download San Bernardino County (FIPS 071), which has a bunch\n",
"of problematic footprints in the Mojave Desert,\n",
"as well as Fennis' [gist of known bad footprints](https://gist.github.com/fennisreed/d7cd238a92c197b5117581caa03317ae)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ed536fc5-9521-4ed2-8448-bbadcf995ca4",
"metadata": {},
"outputs": [],
"source": [
"import contextily # Optional, but useful for basemaps on plots!\n",
"import geopandas\n",
"import pandas\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3e2e1191-e2dc-4492-a33b-16df32f61fc5",
"metadata": {},
"outputs": [],
"source": [
"# Read footprints for San Bernardino County\n",
"footprints = geopandas.read_parquet(\n",
" \"s3://dof-demographics-dev-us-west-2-public/global_ml_building_footprints/parquet/county_fips_071.parquet\",\n",
" storage_options={\"anon\": True},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2a006960-e531-46dc-b61d-80378a805e11",
"metadata": {},
"outputs": [],
"source": [
"# Read data from Fennis' geojson gist of known bad footprints\n",
"bad_footprints = geopandas.read_file(\n",
" \"https://gist.github.com/fennisreed/d7cd238a92c197b5117581caa03317ae/\"\n",
" \"raw/6c3b098bba22aa622320c18d5ba66a99e434f3ac/Footprints_EOI.geojson\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "33c4c667-7e22-4de6-a47b-6b8f71f01e74",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x1000 with 1 Axes>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualize them!\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"bad_footprints.to_crs(epsg=3857).plot(\n",
" ax=ax, alpha=0.5, edgecolor=\"k\"\n",
") # Plot in web Mercator\n",
"contextily.add_basemap(\n",
" ax, source=contextily.providers.CartoDB.Voyager\n",
") # Add a basemap!\n",
"\n",
"ax.set_axis_off()\n",
"ax.set_title(\"Known Bad Footprints\")\n",
"plt.close(fig)\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"id": "6b56b3bb-e49a-4ef1-a85a-7d850600253d",
"metadata": {
"tags": []
},
"source": [
"# Remove bad footprints\n",
"\n",
"We define a utility function `remove_matching_geometries`,\n",
"which takes two GeoDataFrames and removes rows from the first which have matching\n",
"geometries from the second. There are three available strategies for identifying\n",
"matching geometries, which might be appropriate in different circumstances:\n",
"\n",
" \n",
"* `geom_almost_equals` tests if the geometries in the first GeoDataFrame are equal to\n",
" those in the second to some number of decimal points (defaulting to 6).\n",
" This is the default strategy, and is the most forgiving check\n",
" (i.e., it is more likely to consider geometries to be the same).\n",
"* `geom_equals` tests for exact spatial equality of the two geometries.\n",
"* `numeric_equals` tests if all of the numbers in each geometry are the same and\n",
" are in the same order. This is not spatially aware, in contrast to the above two."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0be717e8-e14d-40d1-9e6d-47713e2bb2d7",
"metadata": {},
"outputs": [],
"source": [
"def remove_matching_geometries(gdf1, gdf2, strategy=\"geom_almost_equals\", tolerance=1.e-6):\n",
" \"\"\"\n",
" Remove all geometries from one GeoDataFrame that match geometries in a second one.\n",
"\n",
" There are three available strategies for doing this, which might be appropriate\n",
" in different contexts:\n",
"\n",
" ``geom_almost_equals`` tests if the geometries in gdf1 are equal to those in gdf2\n",
" to some number tolerance (defaulting to 1.e-6). This is the default, and\n",
" is the most forgiving check (i.e., it is more likely to consider geometries to be\n",
" the same).\n",
"\n",
" ``geom_equals`` tests for exact spatial equality of the two geometries.\n",
"\n",
" ``numeric_equals`` tests if all of the numbers in each geometry are the same and\n",
" are in the same order.\n",
"\n",
" Parameters\n",
" ----------\n",
" gdf1: GeoDataFrame\n",
" The first GeoDataFrame\n",
"\n",
" gdf2: GeoDataFrame\n",
" The second GeoDataFrame\n",
"\n",
" strategy: string\n",
" Valid values are \"geom_almost_equals\", \"geom_equals\", and \"numeric_equals\"\n",
"\n",
" tolerance: float\n",
" How many decimals must match before considering geometries to be equal.\n",
" Only relevant for the \"geom_almost_equals\" option.\n",
"\n",
"\n",
" Returns\n",
" -------\n",
" gdf: GeoDataFrame\n",
" gdf1 with matching geometries from gdf2 removed.\n",
" \"\"\"\n",
" import geopandas\n",
" import pandas\n",
"\n",
" if strategy == \"numeric_equals\":\n",
" gdf = pandas.merge(\n",
" gdf1,\n",
" # Only need the geometry column, avoid other possible collisions\n",
" gdf2[[gdf2.geometry.name]],\n",
" left_on=gdf1.geometry.name,\n",
" right_on=gdf2.geometry.name,\n",
" how=\"left\",\n",
" indicator=True,\n",
" )\n",
" return gdf[gdf._merge == \"left_only\"].drop(columns=[\"_merge\"])\n",
"\n",
" elif strategy in (\"geom_equals\", \"geom_almost_equals\"):\n",
" # Duplicate the gdf2 geometry column since spatial joins drop the right geom.\n",
" gdf2_ = gdf2.assign(_bad_geom=gdf2.geometry)[[gdf2.geometry.name, \"_bad_geom\"]]\n",
"\n",
" # Perform a spatial join on intersects, which accomplishes a first\n",
" # approximate pass on equality, finding all potential matches.\n",
" gdf = geopandas.sjoin(gdf1, gdf2_, predicate=\"intersects\", how=\"left\").drop(\n",
" columns=[\"index_right\"]\n",
" )\n",
"\n",
" # Do the more precise comparisons\n",
" if strategy == \"geom_equals\":\n",
" gdf = gdf[~gdf.geometry.geom_equals(gdf._bad_geom)]\n",
" elif strategy == \"geom_almost_equals\":\n",
" gdf = gdf[~gdf.geometry.geom_equals_exact(gdf._bad_geom, tolerance=tolerance)]\n",
"\n",
" return gdf.drop(columns=[\"_bad_geom\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1516bfc6-82a0-46b2-b015-e162da606468",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 600 ms, sys: 116 ms, total: 716 ms\n",
"Wall time: 730 ms\n"
]
}
],
"source": [
"%%time\n",
"\n",
"good_footprints = remove_matching_geometries(\n",
" footprints,\n",
" bad_footprints,\n",
" strategy=\"geom_almost_equals\",\n",
" tolerance=1.e-6,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b23a52c6-5df6-48f4-8ca5-8a101534ff32",
"metadata": {},
"outputs": [],
"source": [
"assert len(good_footprints) == len(footprints) - len(bad_footprints)"
]
},
{
"cell_type": "markdown",
"id": "a45c63b0-60fe-455d-a506-892fab30906a",
"metadata": {},
"source": [
"## Save the good footprints as a file"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "de1d4637-0391-488e-b44e-a89298fcdb0a",
"metadata": {},
"outputs": [],
"source": [
"# use this to save the good footprints as a parquet file\n",
"good_footprints.to_parquet(\"good_footprints.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6bc50d35-3a26-4b09-832b-9216c22d1095",
"metadata": {},
"outputs": [],
"source": [
"# use this to save the good footprints as a geojson file\n",
"good_footprints.to_file(\"good_footprints.geojson\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97e74e57-3be1-45c4-875b-8a0b82c0641e",
"metadata": {},
"outputs": [],
"source": [
"# use this to save the good footprints as a zipped Shapefile\n",
"good_footprints.to_file(\"good_footprints.shz\", driver=\"ESRI Shapefile\")"
]
}
],
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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