improved osm renderning: full building outlines, roads, greenspace, water
This commit is contained in:
Evan Scamehorn
@@ -2,89 +2,146 @@ import osmnx as ox
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import json
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import os
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# ==========================================
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# 1. Configuration
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# ==========================================
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PLACE_NAME = "Wisconsin State Capitol, Madison, USA"
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DIST = 2400 # Meters radius around center
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DIST = 3000
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# 2. Download Data
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# ==========================================
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# 2. Data Fetching
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# ==========================================
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print(f"Downloading data for {PLACE_NAME}...")
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tags = {"building": True}
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# UPDATED FOR V2.0: Access features module explicitly
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# Define tags for different layers
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tags = {
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"building": True,
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"natural": ["water", "bay", "coastline"],
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"landuse": ["grass", "forest", "park", "recreation_ground"],
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"leisure": ["park", "garden"],
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"highway": True,
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}
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try:
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# Try new v2.0 syntax
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# OSMNX v2.0+
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gdf = ox.features.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
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except AttributeError:
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# Fallback for older versions
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# OSMNX < v2.0
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gdf = ox.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
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# 3. Project to meters (Local Grid)
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# UPDATED FOR V2.0: Use GeoPandas native estimation
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# ==========================================
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# 3. Projection & Normalization
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# ==========================================
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print("Projecting to local grid...")
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gdf_proj = gdf.to_crs(gdf.estimate_utm_crs())
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# 4. Prepare Data for THREE.js
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buildings = []
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# Calculate center to normalize coordinates to (0,0)
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# Calculate center for (0,0,0) normalization
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center_x = gdf_proj.geometry.centroid.x.mean()
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center_y = gdf_proj.geometry.centroid.y.mean()
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print("Processing geometry...")
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for _, row in gdf_proj.iterrows():
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if row.geometry.geom_type == "Polygon":
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# Get dimensions
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minx, miny, maxx, maxy = row.geometry.bounds
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width = maxx - minx
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depth = maxy - miny
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# ==========================================
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# 4. Processing Functions
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# ==========================================
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# Get Height (Clean dirty data)
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height = 10 # Default fallback
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# Check for 'height' tag
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if "height" in row and str(row["height"]) != "nan":
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try:
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# Clean strings like "10 m" or "approx 10"
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clean_h = "".join(
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filter(lambda x: x.isdigit() or x == ".", str(row["height"]))
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def get_height(row):
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"""Estimates building height from tags."""
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h = 10.0 # Default
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if "height" in row and str(row["height"]).lower() != "nan":
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try:
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# Extract numeric part
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clean = "".join(
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filter(lambda x: x.isdigit() or x == ".", str(row["height"]))
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)
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h = float(clean)
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except:
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pass
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elif "building:levels" in row and str(row["building:levels"]).lower() != "nan":
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try:
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clean = "".join(
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filter(lambda x: x.isdigit() or x == ".", str(row["building:levels"]))
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)
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h = float(clean) * 3.5
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except:
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pass
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return round(h, 1)
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def parse_polygon(geom):
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"""Extracts exterior coordinates from a Polygon."""
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if geom.is_empty:
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return []
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coords = list(geom.exterior.coords)
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# Simplify slightly to reduce vertex count if needed, or keep raw
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return [[round(x - center_x, 1), round(y - center_y, 1)] for x, y in coords]
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def parse_linestring(geom):
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"""Extracts coordinates from a LineString."""
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if geom.is_empty:
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return []
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coords = list(geom.coords)
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return [[round(x - center_x, 1), round(y - center_y, 1)] for x, y in coords]
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# ==========================================
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# 5. Categorization Loop
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# ==========================================
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output_data = {"buildings": [], "water": [], "parks": [], "roads": []}
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print("Processing geometries...")
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for idx, row in gdf_proj.iterrows():
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geom = row.geometry
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# Handle MultiPolygons by iterating over them
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geoms = [geom] if geom.geom_type in ["Polygon", "LineString"] else []
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if geom.geom_type == "MultiPolygon":
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geoms = list(geom.geoms)
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elif geom.geom_type == "MultiLineString":
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geoms = list(geom.geoms)
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for sub_geom in geoms:
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# 1. BUILDINGS
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if "building" in row and str(row["building"]) != "nan":
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if sub_geom.geom_type == "Polygon":
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output_data["buildings"].append(
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{"shape": parse_polygon(sub_geom), "height": get_height(row)}
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)
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height = float(clean_h)
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except:
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pass
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# Check for 'building:levels' tag
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elif "building:levels" in row and str(row["building:levels"]) != "nan":
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try:
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clean_l = "".join(
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filter(
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lambda x: x.isdigit() or x == ".", str(row["building:levels"])
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)
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)
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height = float(clean_l) * 3.5 # Approx 3.5m per floor
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except:
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pass
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# Normalize position relative to center
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x = (minx + maxx) / 2 - center_x
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z = center_y - (miny + maxy) / 2 # Invert Y for 3D Z-axis
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# 2. WATER
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elif ("natural" in row and row["natural"] in tags["natural"]) or (
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"water" in row and str(row["water"]) != "nan"
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):
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if sub_geom.geom_type == "Polygon":
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output_data["water"].append({"shape": parse_polygon(sub_geom)})
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# Add to array: [x, z, width, depth, height]
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buildings.append(
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[
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round(x, 1),
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round(z, 1),
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round(width, 1),
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round(depth, 1),
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round(height, 1),
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]
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)
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# 3. PARKS / GREENSPACE
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elif ("leisure" in row and row["leisure"] in tags["leisure"]) or (
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"landuse" in row and row["landuse"] in tags["landuse"]
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):
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if sub_geom.geom_type == "Polygon":
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output_data["parks"].append({"shape": parse_polygon(sub_geom)})
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# 5. Save to Public folder
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# 4. ROADS
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elif "highway" in row and str(row["highway"]) != "nan":
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if sub_geom.geom_type == "LineString":
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output_data["roads"].append({"path": parse_linestring(sub_geom)})
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# ==========================================
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# 6. Save File
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# ==========================================
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output_path = os.path.join(os.path.dirname(__file__), "../public/city_data.json")
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# Ensure directory exists just in case
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(buildings, f)
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json.dump(output_data, f)
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print(f"Done! Saved {len(buildings)} buildings to {output_path}")
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print(
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f"Exported:"
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f"\n Buildings: {len(output_data['buildings'])}"
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f"\n Roads: {len(output_data['roads'])}"
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f"\n Water: {len(output_data['water'])}"
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f"\n Parks: {len(output_data['parks'])}"
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)
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print(f"Saved to {output_path}")
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