Use rasterio take geojson Vector rasterization ：

The core is to use rasterio.features.rasterize Function to realize rasterization , Specifically considered ：

1. Vectorization into a single value
2. Vectorization into different values according to a certain field
3. For empty vectors , Vectorization results in 0 Grid of values
4. Writing out grids supports compression

Code implementation ：

Pay attention to SN6 The data has been specially modified

import os.path
from os.path import join, exists
import numpy as np
import rasterio as rio
from rasterio import features, enums
import geopandas as gpd
from tqdm import tqdm


def vector2img(vectorFileName, templateTifFileName, outputFileName, field=None):
    # Read in vector
    vector = gpd.read_file(vectorFileName)

    # Get list of geometries for all features in vector file
    geom = [shapes for shapes in vector.geometry]

    # Open example raster
    raster = rio.open(templateTifFileName)

    if len(geom) > 0:  # only rasterize non-empty vector
        if field is None:
            rasterized = features.rasterize(geom,
                                            out_shape=raster.shape,
                                            fill=0,
                                            out=None,
                                            transform=raster.transform,
                                            all_touched=False,
                                            default_value=1,
                                            dtype=None)
        else:
            # create a numeric unique value for each row
            vector[field] = range(0, len(vector))

            # create tuples of geometry, value pairs, where value is the attribute value you want to burn
            geom_value = ((geom, value) for geom, value in zip(vector.geometry, vector[field]))

            # Rasterize vector using the shape and transform of the raster
            rasterized = features.rasterize(geom_value,
                                            out_shape=raster.shape,
                                            transform=raster.transform,
                                            all_touched=True,
                                            fill=0,  # background value
                                            merge_alg=enums.MergeAlg.replace,
                                            dtype=np.int16)
    else:
        rasterized = np.zeros([raster.height, raster.width]).astype(np.uint8)

    with rio.open(
            outputFileName, "w",
            driver="GTiff",
            transform=raster.transform,
            dtype=rio.uint8,
            count=1,
            width=raster.width,
            height=raster.height,
            compress='lzw') as dst:
        dst.write(rasterized, indexes=1)


def vector2imgBatch(vector_dir, vector_postfix, ref_img_dir, img_postfix, output_dir, lab_postfix, field=None,
                    mismatch=False, vector_lastdir="", ref_img_lastdir=""):
    files = [f for f in os.listdir(vector_dir) if f.endswith(vector_postfix)]
    for file in tqdm(files):
        in_vector = join(vector_dir, file)
        if mismatch:
            ref_img = os.path.join(ref_img_dir, file[:-len(vector_postfix)].replace(vector_lastdir, ref_img_lastdir) + img_postfix)
        else:
            ref_img = os.path.join(ref_img_dir, file[:-len(vector_postfix)].replace(vector_lastdir, ref_img_lastdir) + img_postfix)
        out_label = os.path.join(output_dir,  file[:-len(vector_postfix)].replace(vector_lastdir, ref_img_lastdir) + lab_postfix)
        if exists(in_vector) and exists(ref_img):
            if os.path.exists(out_label):
                print('INFO: vector2img ' + out_label + " exists! Skip.")
            else:
                # print('vector2img: ' + file)
                vector2img(in_vector, ref_img, out_label, field)


if __name__ == '__main__':
    vector_dir = r'Spacenet6_buildings\train\Buildings'
    ref_img_dir = r'Spacenet6_buildings\train\PS-RGB'
    output_dir = r'Spacenet6_buildings\train\PS-RGB_Label'
    vector_postfix = 'geojson'
    img_postfix = 'tif'
    lab_postfix = 'tif'
    vector_lastdir = vector_dir.split('\\')[-1]
    ref_img_lastdir = ref_img_dir.split('\\')[-1]

    if not exists(output_dir):
        os.makedirs(output_dir)
    vector2imgBatch(vector_dir, vector_postfix, ref_img_dir, img_postfix, output_dir, lab_postfix,field=None,
                    mismatch=True, vector_lastdir=vector_lastdir, ref_img_lastdir=ref_img_lastdir)

Reference resources ：

Rasterize Vectors w. Rasterio — Python Open Source Spatial Programming & Remote Sensing (pygis.io)