import gradio as gr import geopandas as gpd import pandas as pd import os import matplotlib.pyplot as plt from shapely.geometry import shape from shapely.ops import unary_union #from datasets import load_dataset #ds = load_dataset('psalama/NYC_sensitive_sites', data_files=data_files) def process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor): # List to store all intersected sensitive sites intersected_sites = [] # List to store all buffers buffers = [] intersection_desc = "" # Iterate over each building in the input file for idx, building in input_gdf.iterrows(): building_name = building.get('building_name', 'Unnamed building') # If the 'building_height' field exists and its value is not null or zero for this building, # use it as the building height. Otherwise, use the default building height provided by the user. if 'building_height' in building and pd.notnull(building['building_height']) and building['building_height'] != 0: building_height_m = building['building_height'] * 0.3048 else: building_height_m = default_building_height_m buffer_distance_m = building_height_m * multiplier_factor # Convert building's geometry to EPSG:3857 for accurate meter-based distance measurement building_geometry = gpd.GeoSeries([building['geometry']], crs="EPSG:4326") building_geometry_m = building_geometry.to_crs("EPSG:3857") # Create a buffer around the building and convert it to a GeoDataFrame building_buffer = building_geometry_m.buffer(buffer_distance_m) building_buffer_gdf = gpd.GeoDataFrame(geometry=building_buffer, crs="EPSG:3857") building_buffer_gdf = building_buffer_gdf.to_crs("EPSG:4326") # Convert back to feet for storing and printing, rounding to the nearest foot building_height_ft = round(building_height_m / 0.3048) buffer_distance_ft = round(buffer_distance_m / 0.3048) # Assign additional attributes building_buffer_gdf['building_name'] = building_name building_buffer_gdf['building_height'] = building_height_ft building_buffer_gdf['buffer_distance'] = buffer_distance_ft buffers.append(building_buffer_gdf) # Check if the buffer intersects with any sensitive sites intersects = gpd.overlay(building_buffer_gdf, sensitive_sites_gdf, how='intersection') if not intersects.empty: building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is in the vicinity of a sensitive site." intersected_sites.append(intersects) else: building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is not in the vicinity of any sensitive sites." if intersection_desc == "": intersection_desc = building_intersect_desc else: intersection_desc += "\n" + building_intersect_desc return buffers, intersected_sites, intersection_desc def get_max_extent(*gdfs): minx = min(gdf.total_bounds[0] for gdf in gdfs) miny = min(gdf.total_bounds[1] for gdf in gdfs) maxx = max(gdf.total_bounds[2] for gdf in gdfs) maxy = max(gdf.total_bounds[3] for gdf in gdfs) return minx, miny, maxx, maxy def create_plot(filename, extent, *gdfs): # takes in unlimited number of gdfs fig, ax = plt.subplots(figsize=(6, 4)) #Sets image size by width & height (in inches) colors = ['blue', 'red', 'green', 'purple', 'orange', 'yellow'] # Extend/improve this list as needed for idx, gdf in enumerate(gdfs): gdf.plot(ax=ax, color=colors[idx % len(colors)]) # Cycle through colors ax.set_xlim(extent[0], extent[2]) ax.set_ylim(extent[1], extent[3]) # Hide axes ax.axis('off') plt.savefig(filename, bbox_inches='tight', pad_inches=0) # remove padding def ss_intersect(geojson1, ss_geoselect, multiplier_factor, default_building_height): # Read the GeoJSON files input_gdf = gpd.read_file(geojson1.name) # Check that CRS is EPSG:4326 if input_gdf.crs.to_epsg() != 4326: raise ValueError("Input GeoJSON files must be in CRS EPSG:4326") if ss_geoselect==0: sensitive_sites_gdf = gpd.read_file("sensitive_sites/NYC_Parks_Properties.geojson") else: sensitive_sites_gdf = gpd.read_file("sensitive_sites/NYC_Parks_Zones.geojson") default_building_height_m = default_building_height * 0.3048 buffers, intersected_sites, intersection_desc = process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor) # Concatenate all buffer GeoDataFrames and save as a GeoJSON file buffers_gdf = pd.concat(buffers, ignore_index=True) buffers_gdf = buffers_gdf.to_crs("EPSG:4326") buffers_gdf.to_file("building_buffers.geojson", driver='GeoJSON') # Concatenate all intersected sensitive sites and save as a GeoJSON file if intersected_sites: intersected_sites_gdf = pd.concat(intersected_sites, ignore_index=True) intersected_sites_gdf = intersected_sites_gdf.to_crs("EPSG:4326") intersected_sites_gdf.to_file("intersected_sensitive_sites.geojson", driver='GeoJSON') else: print("No buildings are in the vicinity of any sensitive sites.") # Perform the union operation if there is more than one buffer if len(buffers) > 1: # Perform a unary union on the geometry column of the GeoDataFrame buffer_union = unary_union(buffers_gdf['geometry']) # Create a new GeoDataFrame from the union result buffer_union_gdf = gpd.GeoDataFrame(geometry=[buffer_union], crs="EPSG:4326") # Save the union GeoDataFrame as a GeoJSON file buffer_union_gdf.to_file("buffer_union.geojson", driver='GeoJSON') # Calculate the maximum extent extent = get_max_extent(input_gdf, buffers_gdf) # Create and save the plot create_plot('output_image.png', extent, buffer_union_gdf, intersected_sites_gdf, input_gdf) # Return the image return 'output_image.png', "building_buffers.geojson", "buffer_union.geojson", intersection_desc iface = gr.Interface( fn=ss_intersect, inputs=[ gr.inputs.File(label="Building Footprints GeoJSON"), gr.Radio(["Parks Properties", "Park Zones"], label="Which Sensitive Sites?", info="From NYC DPR", type="index"), gr.inputs.Slider(minimum=0.0, maximum=10.0, default=4.3, label="Building Height Multiplier"), gr.inputs.Number(default=200, label="Default Building Height"), #Can I make this optional? ], outputs=[ gr.outputs.Image(type="pil", label="Result Image"), gr.outputs.File(label="Building Buffers"), gr.outputs.File(label="Union of Building Buffers"), gr.outputs.Textbox(label="Building intersection descriptions"), ], examples=[ ["files/building4test.geojson", "Parks Properties", 4.3, 200], ], title="Shadow Proximity", description="Upload proposed building footprints in a GeoJSON file and select a numeric value to get the building proximity prediction.", ) iface.launch()