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added example notebooks
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‎CHANGELOG.rst

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--------------------
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Added
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*******
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-support for L2A product level
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- support for L2A product level
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- example notebooks
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*******

‎README.md

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![Examples](img/examples.png)
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## Example (Sentinel-2 Level-1C)
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## Example
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Here's an example on how to compute a cloud and cloud shadow mask from an image. Please note that here we use [ukis-pysat](https://github.com/dlr-eoc/ukis-pysat) for convencience image handling, but you can also work directly with [numpy](https://numpy.org/) arrays. Further examples can be found [here](examples).
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````python
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# read Level-1C image from file, convert digital numbers to TOA reflectance
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# and make sure resolution is 30 m to get best performance
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band_order = ["blue", "green", "red", "nir", "swir16", "swir22"]
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img = Image(data="sentinel2.tif", dimorder="last")
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img.dn2toa(platform=Platform.Sentinel2)
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img.dn2toa(platform=Platform.Sentinel2, wavelength=band_order)
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img.warp(
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resampling_method=0,
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resolution=30,
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# make sure to use these six spectral bands to get best performance
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csmask = CSmask(
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img=img.arr,
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band_order=["blue", "green", "red", "nir", "swir16", "swir22"],
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band_order=band_order,
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nodata_value=0,
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)
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‎examples/landsat8_l1c.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Segment clouds and cloud shadows in Landsat-8 images (L1C)\n",
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"This notebook shows an example on how to use [ukis-csmask](https://github.com/dlr-eoc/ukis-csmask) to segment clouds and cloud shadows in Level-1C images from Landsat-8. Images are loaded from local file system. Here we use [ukis-pysat](https://github.com/dlr-eoc/ukis-pysat) for convencience image handling, but you can also work directly with [numpy](https://numpy.org/) arrays.\n",
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"\n",
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"> NOTE: to run this notebook, we first need to install some additional dependencies for image handling\n",
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"```shell\n",
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"pip install ukis-pysat[complete]\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "703f3744-902d-470b-a80f-9a8d3ea08dfa",
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"metadata": {},
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"outputs": [],
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"source": [
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"import rasterio\n",
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"import numpy as np\n",
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"\n",
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"from pathlib import Path\n",
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"from ukis_csmask.mask import CSmask\n",
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"from ukis_pysat.raster import Image, Platform"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c9cd86e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# user settings\n",
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"data_path = \"/your_data_path/\"\n",
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"L8_file_prefix = \"LC08_L1TP_191015_20210428_20210507_02_T1\"\n",
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"product_level = \"l1c\"\n",
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"band_order = [\"blue\", \"green\", \"red\", \"nir\", \"swir16\", \"swir22\"]\n",
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"providers = [\"CUDAExecutionProvider\"]\n",
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"out_dir = \"ukis-csmask/examples\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8ca03c78-1e24-479c-9786-a1b43206a08b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set Landsat 8 source path and prefix (example)\n",
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"data_path = data_path + L8_file_prefix + \"/\"\n",
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"mtl_file = data_path + L8_file_prefix + \"_MTL.txt\"\n",
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"\n",
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"# stack [B2:'Blue', B3:'Green', B4:'Red', B5:'NIR', B6:'SWIR1', B7:'SWIR2'] as numpy array\n",
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"L8_band_files = [data_path + L8_file_prefix + \"_B\" + x + \".TIF\" for x in [str(x + 2) for x in range(6)]]\n",
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"\n",
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"# >> adopted from https://gis.stackexchange.com/questions/223910/using-rasterio-or-gdal-to-stack-multiple-bands-without-using-subprocess-commands\n",
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"# read metadata of first file\n",
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"with rasterio.open(L8_band_files[0]) as src0:\n",
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" meta = src0.meta\n",
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"# update meta to reflect the number of layers\n",
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"meta.update(count=len(L8_band_files))\n",
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"# read each layer and append it to numpy array\n",
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"L8_bands = []\n",
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"for id, layer in enumerate(L8_band_files, start=1):\n",
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" with rasterio.open(layer) as src1:\n",
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" L8_bands.append(src1.read(1))\n",
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"L8_bands = np.stack(L8_bands, axis=2)\n",
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"# <<\n",
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"\n",
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"img = Image(data=L8_bands, crs=meta[\"crs\"], transform=meta[\"transform\"], dimorder=\"last\")\n",
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"img.dn2toa(platform=Platform.Landsat8, mtl_file=mtl_file, wavelengths=band_order)\n",
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"img.warp(resampling_method=0, resolution=30, dst_crs=img.dataset.crs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b568942-84e6-4baf-b490-a213b3787f80",
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"metadata": {},
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"outputs": [],
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"source": [
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"# compute cloud and cloud shadow mask\n",
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"csmask = CSmask(\n",
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" img=img.arr,\n",
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" band_order=band_order,\n",
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" product_level=product_level,\n",
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" nodata_value=0,\n",
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" invalid_buffer=4,\n",
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" intra_op_num_threads=0,\n",
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" inter_op_num_threads=0,\n",
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" providers=providers,\n",
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" batch_size=1,\n",
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")\n",
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"\n",
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"# access cloud and cloud shadow mask as numpy array\n",
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"csm = csmask.csm\n",
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"\n",
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"# access valid mask as numpy array\n",
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"valid = csmask.valid"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68eb9c30-06f7-409e-914d-21e00f45de99",
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"metadata": {},
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"outputs": [],
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"source": [
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"# convert results to ukis-pysat Image\n",
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"# this assigns back the georeference\n",
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"csm = Image(csm, transform=img.dataset.transform, crs=img.dataset.crs, dimorder=\"last\")\n",
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"valid = Image(valid, transform=img.dataset.transform, crs=img.dataset.crs, dimorder=\"last\")\n",
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"\n",
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"# write results to file\n",
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"csm.write_to_file(\n",
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" path_to_file=Path(out_dir) / Path(f\"{L8_file_prefix}_csm.tif\"),\n",
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" dtype=csm.dtype,\n",
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" driver=\"COG\",\n",
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" compress=\"LZW\",\n",
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" kwargs={\"BLOCKSIZE\": 512, \"BIGTIFF\": \"IF_SAFER\"},\n",
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")\n",
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"valid.write_to_file(\n",
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" path_to_file=Path(out_dir) / Path(f\"{L8_file_prefix}_valid.tif\"),\n",
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" dtype=valid.dtype,\n",
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" driver=\"COG\",\n",
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" compress=\"LZW\",\n",
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" kwargs={\"BLOCKSIZE\": 512, \"BIGTIFF\": \"IF_SAFER\"},\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/landsat_l2a.ipynb

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"metadata": {},
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"source": [
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"# Segment clouds and cloud shadows in Landsat images (L2A)\n",
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"This notebook shows an example on how to use [ukis-csmask]() to segment clouds and cloud shadows in Level-2A images from Landsat-9, Landsat-8, Landsat-7 and Landsat-5 satellites. Images are acquired from [Planetary Computer]() and are prepared according to [ukis-csmask]() requirements.\n",
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"This notebook shows an example on how to use [ukis-csmask](https://github.com/dlr-eoc/ukis-csmask) to segment clouds and cloud shadows in Level-2A images from Landsat-9, Landsat-8, Landsat-7 and Landsat-5 satellites. Images are acquired from [Planetary Computer](https://planetarycomputer.microsoft.com) and are preprocessed.\n",
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"\n",
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"> NOTE: to run this notebook, we first need to install some additional dependencies for work with Planetary Computer\n",
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"```shell\n",
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"pip install planetary_computer pystac-client odc-stac tqdm\n",
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"pip install -e git+https://github.com/dlr-eoc/ukis-csmask/tree/l2a#egg=ukis-csmask[gpu]\n",
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"$ pip install planetary_computer rioxarray pystac-client odc-stac tqdm\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "703f3744-902d-470b-a80f-9a8d3ea08dfa",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import planetary_computer as pc\n",
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"import rioxarray\n",
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"\n",
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"from odc.stac import load\n",
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"from pathlib import Path\n",
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"from pystac_client import Client\n",
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"from tqdm import tqdm\n",
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"from ukis_csmask.mask import CSmask"
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"from ukis_csmask.mask import CSmask\n",
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"from xarray import DataArray"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"id": "8ca03c78-1e24-479c-9786-a1b43206a08b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# user settings\n",
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"stac_api_endpoint = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
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"collections = [\"landsat-c2-l2\"]\n",
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"ids = [\n",
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" \"LC09_L2SP_194027_20240604_02_T1\",\n",
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" \"LC08_L2SP_191027_20240607_02_T1\",\n",
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" \"LE07_L2SP_192027_20050805_02_T1\",\n",
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" \"LT05_L2SP_192026_20100726_02_T1\",\n",
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" \"LC09_L2SP_192027_20240403_02_T2\",\n",
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" \"LC08_L2SP_192027_20240801_02_T1\",\n",
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" \"LE07_L2SP_192027_20000722_02_T1\",\n",
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" \"LT05_L2SP_192027_20100726_02_T1\",\n",
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"]\n",
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"bbox = [11.016, 47.666, 12.238, 48.472]\n",
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"bbox = [11.540, 47.463, 12.117, 47.872]\n",
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"product_level = \"l2a\"\n",
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"band_order = [\"blue\", \"green\", \"red\", \"nir\", \"swir16\", \"swir22\"]\n",
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"providers = [\"CUDAExecutionProvider\"]"
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"providers = [\"CUDAExecutionProvider\"]\n",
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"out_dir = \"ukis-csmask/examples\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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"id": "7b568942-84e6-4baf-b490-a213b3787f80",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Search returned 4 item(s)\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# search catalog by scene id\n",
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"catalog = Client.open(stac_api_endpoint)\n",
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"cell_type": "code",
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"Predict images: 0%| | 0/4 [00:00<?, ?it/s]"
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]
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"\n"
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},
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: '/mnt/data1/Workspace/python/ukis-csmask/src/ukis-csmask/ukis_csmask/model_6b_l2a.json'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[18], line 33\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28mprint\u001b[39m(arr\u001b[38;5;241m.\u001b[39mshape, arr\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# compute cloud and cloud shadow mask\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m csmask \u001b[38;5;241m=\u001b[39m \u001b[43mCSmask\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmoveaxis\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mband_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mband_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mproduct_level\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproduct_level\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodata_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[43m \u001b[49m\u001b[43minvalid_buffer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43mintra_op_num_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_op_num_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43mproviders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproviders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;66;03m# access cloud and cloud shadow mask\u001b[39;00m\n\u001b[1;32m 46\u001b[0m csmask_csm \u001b[38;5;241m=\u001b[39m csmask\u001b[38;5;241m.\u001b[39mcsm\n",
119-
"File \u001b[0;32m/mnt/data1/Workspace/python/ukis-csmask/src/ukis-csmask/ukis_csmask/mask.py:90\u001b[0m, in \u001b[0;36mCSmask.__init__\u001b[0;34m(self, img, band_order, product_level, nodata_value, invalid_buffer, intra_op_num_threads, inter_op_num_threads, providers, batch_size)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 87\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mband_order must contain at least \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnir\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mand for better performance also \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mswir16\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mswir22\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 89\u001b[0m )\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwith_suffix\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 91\u001b[0m model_dict \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# start onnx inference session and load model\u001b[39;00m\n",
120-
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/data1/Workspace/python/ukis-csmask/src/ukis-csmask/ukis_csmask/model_6b_l2a.json'"
121-
]
122-
}
123-
],
78+
"outputs": [],
12479
"source": [
125-
"import rioxarray\n",
126-
"\n",
12780
"for item in tqdm(items, total=items_cnt, desc=\"Predict images\"):\n",
12881
" # near infrared band has different alias in landsat collections\n",
12982
" bands = [b.replace(\"nir\", \"nir08\") for b in band_order]\n",
@@ -143,15 +96,13 @@
14396
" .drop_vars(\"time\")\n",
14497
" )\n",
14598
" arr = arr.rename({\"variable\": \"band\"})\n",
146-
" arr.rio.to_raster(raster_path=\"l8.tif\", driver=\"COG\")\n",
14799
"\n",
148100
" # use band-specific rescale factors to convert DN to reflectance\n",
149101
" for idx, band_name in enumerate(bands):\n",
150102
" band_info = item.assets[band_name].extra_fields[\"raster:bands\"][0]\n",
151103
" arr[idx, :, :] = arr.sel(band=str(band_name)).astype(np.float32) * band_info[\"scale\"]\n",
152104
" arr[idx, :, :] += band_info[\"offset\"]\n",
153105
" arr[idx, :, :] = arr[idx, :, :].clip(min=0.0, max=1.0)\n",
154-
" print(arr.shape, arr.dtype)\n",
155106
"\n",
156107
" # compute cloud and cloud shadow mask\n",
157108
" csmask = CSmask(\n",
@@ -166,11 +117,14 @@
166117
" batch_size=1,\n",
167118
" )\n",
168119
"\n",
169-
" # access cloud and cloud shadow mask\n",
170-
" csmask_csm = csmask.csm\n",
171-
"\n",
172-
" # access valid mask\n",
173-
" csmask_valid = csmask.valid"
120+
" # write image, csm and valid mask to file\n",
121+
" arr.rio.to_raster(raster_path=Path(out_dir) / Path(f\"{item.id}.tif\"), driver=\"COG\")\n",
122+
" DataArray(np.squeeze(np.moveaxis(csmask.csm, -1, 0)), coords=arr.sel(band=arr[\"band\"][0]).coords).rio.to_raster(\n",
123+
" raster_path=Path(out_dir) / Path(f\"{item.id}_csm.tif\"), driver=\"COG\"\n",
124+
" )\n",
125+
" DataArray(np.squeeze(np.moveaxis(csmask.valid, -1, 0)), coords=arr.sel(band=arr[\"band\"][0]).coords).rio.to_raster(\n",
126+
" raster_path=Path(out_dir) / Path(f\"{item.id}_valid.tif\"), driver=\"COG\"\n",
127+
" )"
174128
]
175129
}
176130
],

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