Raster to Dataframe

load raster file as a Dataframe 

from rasterio import features
from rasterio.plot import show
from rasterio.windows import Window, transform

Vector to Raster mask

convert vectors into raster masks where each pixel is a numerical representation of a label/category

generate_mask


def generate_mask(
    tiff_file, # Path to reference TIFF file
    shape_file, # Path to shapefile
    output_file, # Path to output file
    labels_column, # Feature in the shapefile that contains labels/categories
    labels_dict:Dict, # Dictionary of desired labels and assigned values for the mask
    plot:bool=False, # Plots vector, reference TIF image, and raster mask output
):

Generates a segmentation mask for one TIFF image. Returns: image (np.array): A binary mask as a numpy array

Raster to Dataframe

Reads the bands for each image in the list and returns a dataframe where each band is one column with the image name as a suffix for column name.

read_bands


def read_bands(
    image_list:List, mask:str
):

Test data

Generating a raster mask

# Get filepaths
tiff_file = "../data/vector_to_raster_mask_sample/cabanglasan.tif"
shape_file = "../data/vector_to_raster_mask_sample/labels_20220816.gpkg"
target_file = shape_file.replace("gpkg", "tiff")

Given a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.

gpd.read_file(shape_file).head(3)
year label uid ADM3_EN ADM3_PCODE ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE geometry
0 2017.0 mining 72_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95961 9.03303 0, 117.959...
1 2017.0 mining 71_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95507 9.03809 0, 117.955...
2 2017.0 mining 70_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95663 9.03869 0, 117.956...

And a dictionary with your desired labels and assigned values in creating a mask

labels = {
    "mining": 1,
    "neg": 2,
    "agriculture": 3,
    "product_extraction": 4,
    "kaingin": 5,
    "biophysical": 6,
}

Input them in the generate_mask function to create a raster mask of the same dimension as the reference raster image

# Generate masks for a file
masks, grids, values = generate_mask(
    tiff_file=tiff_file,
    shape_file=shape_file,
    output_file=target_file,
    labels_column="label",
    labels_dict=labels,
    plot=True,
)

masks
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
grids
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
values
{'mining': 1,
 'neg': 2,
 'agriculture': 3,
 'product_extraction': 4,
 'kaingin': 5,
 'biophysical': 6}

Choose raster images to convert into a dataframe and use the generated raster mask to add the labels the converted dataframe.

tiff_files = ["../data/vector_to_raster_mask_sample/cabanglasan.tif"]
mask_file = "../data/vector_to_raster_mask_sample/labels_20220816.tiff"
data = read_bands(tiff_files, mask_file)
data
B1_0 B2_0 B3_0 B4_0 B5_0 B6_0 B7_0 B8_0 B9_0 B10_0 B11_0 B12_0 label
0 0.1198 0.09635 0.09330 0.0698 0.10665 0.20250 0.2490 0.23525 0.28125 0.0377 0.19925 0.1002 0
1 0.1198 0.09580 0.09245 0.0708 0.10665 0.20250 0.2490 0.23925 0.28125 0.0377 0.19925 0.1002 0
2 0.1148 0.09420 0.09460 0.0707 0.10380 0.20395 0.2478 0.23150 0.27165 0.0385 0.18240 0.0902 0
3 0.1148 0.09190 0.08850 0.0631 0.10380 0.20395 0.2478 0.23300 0.27165 0.0385 0.18240 0.0902 0
4 0.1148 0.09350 0.09080 0.0643 0.10565 0.20830 0.2466 0.24205 0.26990 0.0385 0.18050 0.0894 0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
775824 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775825 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775826 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775827 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775828 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0

775829 rows × 13 columns