conversion 2D raster to 3D

RakhshindaBano

I am trying to generate a map with a 2D raster as basemap (observed landuse) and overlay it with 3D error bars (containing the incorrect land use categories that model predicted). So it involves conversion of one of the 2D files to 3D first

 

I have been trying both in arcgis pro and arc scene

• In Arc Scene I first created a height column in attribute table as the raster doesn't have elevation information. when I tried this link and increased the value of vertical exaggeration, it also changed the 2D base map which I wanted to keep as it is. 
  

  I am looking to get something like the map at the bottom but the vertical bars come from a different raster with the same spatial boundary as base map

   

  • The python code I have been using looks like below and it gives me a map skipping the basemap completely and very high error bars. I am trying to figure out what am I missing 

    import arcpy

    import pandas as pd

    import numpy as np

    import matplotlib.pyplot as plt

    from matplotlib.colors import ListedColormap

    import os

    from mpl_toolkits.mplot3d import Axes3D  # Import for 3D plotting

     

    # Switch to 'Agg' backend for non-interactive use, better for saving figures

    import matplotlib

    matplotlib.use('Agg')

     

    # Input file paths

    raster_a_path = r"C:\Users\xyz\ xyz \ xyz \ xyz \ xyz.tif"

    raster_c_path = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz.tif"

    csv_path = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz.csv"

    output_folder = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz”

     

    # Load the CSV file containing land use types and colors

    legend_df = pd.read_csv(csv_path)

    legend_df['LandUseType'] = legend_df['LandUseType'].str.strip()  # Clean up data

     

    # Ensure all color codes are valid hexadecimal

    def validate_color(color):

        if isinstance(color, str) and color.startswith('#') and len(color) == 7:

            try:

                int(color[1:], 16)  # Try converting from hex to RGB

                return True

            except ValueError:

                return False

        return False

     

    legend_df = legend_df[legend_df['Color'].apply(validate_color)]

     

    # Convert values and colors to a dictionary

    value_to_color = dict(zip(legend_df['Value'], legend_df['Color']))

     

    # Convert colors to RGB tuples for use with matplotlib, adjust brightness for 3D bars

    colors_rgb = [tuple(min(int(color.strip("#")[i:i+2], 16) / 255.0 * 1.5, 1.0) for i in (0, 2, 4)) for color in legend_df['Color']]

    cmap = ListedColormap(colors_rgb)

     

    # Load raster datasets

    raster_a = arcpy.Raster(raster_a_path)

    raster_c = arcpy.Raster(raster_c_path)

     

    # Convert raster_a and raster_c to NumPy arrays

    raster_a_array = arcpy.RasterToNumPyArray(raster_a)

    raster_c_array = arcpy.RasterToNumPyArray(raster_c)

     

    # Check if dimensions match

    if raster_a_array.shape != raster_c_array.shape:

        raise ValueError(f"Dimensions of raster_a ({raster_a_array.shape}) and raster_c ({raster_c_array.shape}) do not match")

     

    # Mask NoData values (-9999) in raster_a

    no_data_value = -9999

    raster_a_masked = np.ma.masked_equal(raster_a_array, no_data_value)

     

    # Adjust extent based on the raster's spatial reference

    extent = [raster_a.extent.XMin, raster_a.extent.XMax, raster_a.extent.YMin, raster_a.extent.YMax]

     

    # Plot Raster A in 2D

    fig = plt.figure(figsize=(10, 10))

    ax_2d = fig.add_subplot(111)

    img_2d = ax_2d.imshow(raster_a_masked, cmap=cmap, extent=extent, interpolation='none')

     

    # Overlay Raster C in 3D (vertical bars)

    ax_3d = fig.add_subplot(111, projection='3d')

     

    # Create grid for raster_c

    x = np.arange(raster_c_array.shape[1])

    y = np.arange(raster_c_array.shape[0])

    x, y = np.meshgrid(x, y)

     

    # Normalize height of 3D bars to a reasonable scale

    heights = raster_c_array / np.nanmax(raster_c_array) * 10  # Scale heights for visibility

     

    # Plot each bar as a vertical block

    for i in range(raster_c_array.shape[0]):

        for j in range(raster_c_array.shape[1]):

            if raster_c_array[i, j] != no_data_value:

                ax_3d.bar3d(x[i, j], y[i, j], 0, 1, 1, heights[i, j],

                            color=cmap(raster_c_array[i, j] % len(colors_rgb)), alpha=0.5)

     

    # Set aspect ratios and remove the 3D axis, background grids, and labels

    ax_3d.set_axis_off()

     

    # Set the view angle for 3D projection

    ax_3d.view_init(elev=50, azim=-60)

     

    # Pause to ensure rendering completes before saving

    plt.draw()

    plt.pause(0.1)

     

    # Construct the full path for saving the plot

    output_path = os.path.join(output_folder, "output_base_map_with_3D_overlay.png")

    # Save the plot to the specified folder

    plt.savefig(output_path, bbox_inches='tight', dpi=300)

    print(f"Plot saved successfully to: {output_path}")