Run inference on a Sentinel-2 image from the Google Earth Engine¶
- Last Modified: 30-08-2024
- Author: Gonzalo Mateo-GarcĂa
CloudSEN12 ¶
This notebook shows how to run the CloudSEN12 model over an image downloaded from the Google Earth Engine. Hence to run this notebook you a Google Earth Engine account.
We will run the models UnetMobV2_V1 and UnetMobV2_V2 with all bands proposed in:
Aybar, C., Ysuhuaylas, L., Loja, J., Gonzales, K., Herrera, F., Bautista, L., Yali, R., Flores, A., Diaz, L., Cuenca, N., Espinoza, W., Prudencio, F., Llactayo, V., Montero, D., Sudmanns, M., Tiede, D., Mateo-GarcĂa, G., & GĂłmez-Chova, L. (2022). CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2. Scientific Data, 9(1), Article 1. DOI: 10.1038/s41597-022-01878-2
Aybar, C., Bautista, L., Montero, D., Contreras, J., Ayala, D., Prudencio, F., Loja, J., Ysuhuaylas, L., Herrera, F., Gonzales, K., Valladares, J., Flores, L. A., Mamani, E., Quiñonez, M., Fajardo, R., Espinoza, W., Limas, A., Yali, R., Alcántara, A., Leyva, M., Loayza-Muro, M., Willems, M., Mateo-GarcĂa, G. & GĂłmez-Chova, L. (2024). CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2. Data in Brief, 110852. DOI: 10.1016/j.dib.2024.110852
pip install georeader-spaceml earthengine-api cloudsen12_models
# cloudsen12_models is not a georeader dependency (it depends on georeader),
# so install it in the running kernel.
%pip install cloudsen12_models
from cloudsen12_models import cloudsen12
import ee
import matplotlib.pyplot as plt
from georeader import plot
from shapely.geometry import box
from georeader.readers import ee_image
from georeader.readers import ee_image
# Initialize Earth Engine from environment variables (service-account key,
# token, or local credentials); see examples/README.md and .env.sample.
ee_image.initialize()
collection_name = "COPERNICUS/S2_HARMONIZED"
tile = "S2A_MSIL1C_20240417T064631_N0510_R020_T40RCN_20240417T091941"
img_col = ee.ImageCollection(collection_name)
image = img_col.filter(ee.Filter.eq("PRODUCT_ID", tile)).first()
info_img = image.getInfo()
# info_img
%%time
from rasterio import Affine
# projgee = image.select("B2").projection().getInfo()
aoi = box(55.325, 25.225, 55.415, 25.28)
bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']
crs = info_img["bands"][1]["crs"]
transform = Affine(*info_img["bands"][1]["crs_transform"])
img_local = ee_image.export_image(info_img['id'],
crs=crs, transform=transform,
bands_gee=bands,
geometry=aoi)
img_local
swirnirred = (img_local.isel({"band": [bands.index(b) for b in ["B11","B8","B4"]]}) / 4_500.).clip(0,1)
plot.show(swirnirred)
UNetMobV2_V1¶
Model trained on the first version of CloudSEN12 Aybar et al. 2022.
model = cloudsen12.load_model_by_name(name="UNetMobV2_V1", weights_folder="cloudsen12_models")
cloudsen12.MODELS_CLOUDSEN12.keys()
cloudmask = model.predict(img_local/10_000)
fig, ax = plt.subplots(1,2,figsize=(14,5),sharey=True, tight_layout=True)
plot.show(swirnirred,ax=ax[0])
cloudsen12.plot_cloudSEN12mask(cloudmask,ax=ax[1])
fig.savefig("example_flood_dubai_2024.png")
UnetMobV2_V2¶
Model trained on the second version of CloudSEN12 (called CloudSEN12+) Aybar et al. 2024.
modelv2 = cloudsen12.load_model_by_name(name="UNetMobV2_V2", weights_folder="cloudsen12_models")
cloudmaskv2 = modelv2.predict(img_local/10_000)
fig, ax = plt.subplots(1,2,figsize=(14,5),sharey=True, tight_layout=True)
plot.show(swirnirred,ax=ax[0])
cloudsen12.plot_cloudSEN12mask(cloudmaskv2,ax=ax[1])
Sentinel-2 L2A¶
Run the model for L2A images.
from shapely.geometry import mapping
collection_name = "COPERNICUS/S2_SR_HARMONIZED"
tile = "S2A_MSIL2A_20240417T064631_N0510_R020_T40RCN_20240417T091941"
aoi_ee = ee.Geometry(mapping(aoi))
img_col = ee.ImageCollection(collection_name).filterDate('2024-04-17', '2024-04-18').filterBounds(aoi_ee)
image = img_col.first()
info_imgl2a = image.getInfo()
# info_imgl2a
bandsl2a = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']
crs = info_imgl2a["bands"][1]["crs"]
transform = Affine(*info_imgl2a["bands"][1]["crs_transform"])
img_local_l2a = ee_image.export_image(info_imgl2a['id'],
crs=crs, transform=transform,
bands_gee=bandsl2a,
geometry=aoi)
img_local_l2a
swirnirred_l2a = (img_local_l2a.isel({"band": [bandsl2a.index(b) for b in ["B11","B8","B4"]]}) / 4_500.).clip(0,1)
plot.show(swirnirred_l2a)
modell2a = cloudsen12.load_model_by_name(name="cloudsen12l2a", weights_folder="cloudsen12_models")
cloudmask_l2a = modell2a.predict(img_local_l2a/10_000)
fig, ax = plt.subplots(1,2,figsize=(14,5),sharey=True, tight_layout=True)
plot.show(swirnirred_l2a,ax=ax[0])
cloudsen12.plot_cloudSEN12mask(cloudmask_l2a,ax=ax[1])
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.
This notebook is released under a Creative Commons non-commercial licence.
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-GarcĂa, Gonzalo and GĂłmez-Chova, Luis},
month = aug,
year = {2024},
pages = {110852},
}
@article{aybar_cloudsen12_2022,
title = {{CloudSEN12}, a global dataset for semantic understanding of cloud and cloud shadow in {Sentinel}-2},
volume = {9},
issn = {2052-4463},
url = {https://www.nature.com/articles/s41597-022-01878-2},
doi = {10.1038/s41597-022-01878-2},
number = {1},
urldate = {2023-01-02},
journal = {Scientific Data},
author = {Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and Espinoza, Wendy and Prudencio, Fernando and Llactayo, Valeria and Montero, David and Sudmanns, Martin and Tiede, Dirk and Mateo-GarcĂa, Gonzalo and GĂłmez-Chova, Luis},
month = dec,
year = {2022},
pages = {782},
}