Datasets:

fudong03

/

Tiny-CropNet

like 3

Hi, thank you for reaching out.

I don't fully understand your point about the 12-channel thing.

I recommend you use the following code for visualizing the Sentinel-2 Imagery for a better understanding of the structure of the H5 files:

def display_sample_image(base_path, h5_file_path, FIPS, date, grid_num):
    """Function to return grid image based on a provided h5 file and parameters

    Parameters
    ----------
    base_path: (String) path to where the Sentinel data was downloaded
    h5_file_path: (String) path to the particular h5 file that has images to display
    FIPS: (String) the FIPS code of the county you want to represent
    date: (String) the date that you want images from (Example: "2021-04-01")
    grid_num: (integer) the grid number you want to display

    Returns
    ----------
    None
    """
    # read the h5 file
    h5_data = h5py.File(base_path + h5_file_path, 'r')

    # get the particular county information
    FIPS_data = h5_data[FIPS]

    # get the particular date information
    date_data = FIPS_data[date]

    # print the state of the grid
    state = date_data["state"][0].decode()
    print("State of the Grid:", state)
    print()

    # print the county of the grid
    county = date_data["county"][0].decode()
    print("County of the Grid:", county)
    print()

    # print the coordinates of the particular grid
    coordinates = date_data["coordinates"][grid_num]
    print("Coordinates of the grid:")
    print("lower left corner lat:", round(coordinates[0][0], 2))
    print("lower left corner lon:", round(coordinates[0][1], 2))
    print("upper right corner lat:", round(coordinates[1][0], 2))
    print("upper right corner lon:", round(coordinates[1][1], 2))

    # display the image of the chosen grid
    image_data = date_data["data"][grid_num]

    # show the image
    plt.imshow(image_data)
    plt.show()

display_sample_image(base_path, file_path, "10005", "2022-08-15", 2)