Tiling and stitching segmentation outputs¶
- Author: Gonzalo Mateo-García
This tutorial shows how to run an AI model by fix-size tiles following the recommendations of Huang et al. 2018:
B. Huang, D. Reichman, L. M. Collins, K. Bradbury, and J. M. Malof, Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations, arXiv:1805.12219
pip install cloudsen12_models georeader-spaceml fsspec gcsfs
# cloudsen12_models is not a georeader dependency (it depends on georeader),
# so install it in the running kernel.
%pip install cloudsen12_models
import os
from georeader.readers import S2_SAFE_reader
from georeader import read
from shapely.geometry import box
from georeader import plot
from cloudsen12_models import cloudsen12
from georeader import slices
from rasterio import windows
from georeader.geotensor import GeoTensor
from georeader import window_utils
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
os.environ["GS_NO_SIGN_REQUEST"] = "YES"
Load sample data¶
%%time
# Download a S2 product
safe_file = "S2A_MSIL1C_20240417T064631_N0510_R020_T40RCN_20240417T091941.SAFE"
s2_safe_folder = S2_SAFE_reader.s2_public_bucket_path(safe_file, check_exists=False)
print(f"File is located at: {s2_safe_folder}")
s2obj = S2_SAFE_reader.s2loader(s2_safe_folder, out_res=10)
tempfolder = "../../deleteme"
os.makedirs(tempfolder,exist_ok=True)
s2obj = s2obj.cache_product_to_local_dir(tempfolder)
s2obj
%%time
aoi = box(55.325, 25.21, 55.515, 25.31)
data = read.read_from_polygon(s2obj, aoi, crs_polygon="EPSG:4326", trigger_load=True)
data
Load model¶
We will use the CloudSEN12 cloud detection model as sample.
model = cloudsen12.load_model_by_name(name="UNetMobV2_V2",
weights_folder="../../cloudsen12_models")
Tiling and stitching loop¶
overlap_total_pixels = 32 # on each side!
window_size_predict_nn = 512
input_tensor = data
windows_write = slices.create_windows(input_tensor.shape[-2:],
window_size=(window_size_predict_nn-overlap_total_pixels,
window_size_predict_nn-overlap_total_pixels),
overlap=(0, 0),
trim_incomplete=True)
output_tensor = GeoTensor(np.full(input_tensor.shape[-2:],fill_value=0,dtype=np.float32),
transform=input_tensor.transform,
crs=input_tensor.crs,
fill_value_default = 0)
for w_write in tqdm(windows_write):
w_read = window_utils.pad_window_to_size(w_write,
size=(window_size_predict_nn,
window_size_predict_nn))
input_tensor_iter = read.read_from_window(input_tensor,
window=w_read,
boundless=True,
trigger_load=True)
valids_iter = np.all(input_tensor_iter.values != input_tensor_iter.fill_value_default,
axis=0)
if not np.any(valids_iter):
# print("All invalid")
continue
slice_save = window_utils.slice_save_for_pred(w_read, w_write)
out = model.predict(input_tensor_iter.values / 10_000)
out[~valids_iter] = output_tensor.fill_value_default
out_save = out[slice_save]
output_tensor.write_from_window(out_save, window=w_write)
# For plotting
bands = s2obj.bands
swirnirred = (data.isel({"band": [bands.index(b) for b in ["B11","B08","B04"]]}) / 4_500.).clip(0,1)
fig, ax = plt.subplots(1,2,figsize=(14,5),sharey=True, tight_layout=True)
plot.show(swirnirred,ax=ax[0])
cloudsen12.plot_cloudSEN12mask(output_tensor,ax=ax[1])
Licence¶
This notebook is released under a Creative Commons non-commercial licence.
The cloudsen12_models package is published under a GNU Lesser GPL v3 licence
The CloudSEN12 database and all pre-trained models are released under a Creative Commons non-commercial licence. For using the models in comercial pipelines written consent by the authors must be provided.
If you find this work useful please cite:
@article{aybar_cloudsen12_2024,
title = {{CloudSEN12}+: {The} largest dataset of expert-labeled pixels for cloud and cloud shadow detection in {Sentinel}-2},
issn = {2352-3409},
url = {https://www.sciencedirect.com/science/article/pii/S2352340924008163},
doi = {10.1016/j.dib.2024.110852},
journal = {Data in Brief},
author = {Aybar, Cesar and Bautista, Lesly and Montero, David and Contreras, Julio and Ayala, Daryl and Prudencio, Fernando and Loja, Jhomira and Ysuhuaylas, Luis and Herrera, Fernando and Gonzales, Karen and Valladares, Jeanett and Flores, Lucy A. and Mamani, Evelin and Quiñonez, Maria and Fajardo, Rai and Espinoza, Wendy and Limas, Antonio and Yali, Roy and Alcántara, Alejandro and Leyva, Martin and Loayza-Muro, Rau´l and Willems, Bram and Mateo-García, Gonzalo and Gómez-Chova, Luis},
month = aug,
year = {2024},
pages = {110852},
}