A raster conversion and segmentation mask vectorization tool for KappaZeta cloudmask.
The following system dependencies are needed:
- g++8 or later
- CMake 3.4.3 or later
- cget (https://github.com/pfultz2/cget)
- OpenJPEG (
libopenjp2-7-dev) - GDAL (
libgdal-dev,gdal-bin,gdal-data) - Expat (
libexpat1-dev) - GraphicsMagick++ (
libgraphicsmagick++1-dev) - NetCDF (
libnetcdf-dev) - nlohmann's JSON (https://github.com/nlohmann/json)
- CppUnit (
libcppunit-dev) - Doxygen (for documentation only)
On Ubuntu, the following commands could be used to install the dependencies:
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update
sudo apt-get install cmake gcc-9 g++-9 python3-pip libgdal-dev gdal-bin libexpat1-dev libgraphicsmagick++1-dev libcppunit-dev
pip3 install cget
On Fedora or CentOS, run
sudo yum install cmake gcc gcc-c++ python3-pip openjpeg2-devel libpng-devel gdal gdal-devel expat-devel GraphicsMagick-c++-devel netcdf-devel libjpeg-turbo-devel jbigkit-libs jbigkit-devel m4 cppunit-devel libtiff-devel
Make sure that your GDAL_DATA environment variable has been set, according to your GDAL version, which is indicated by the placeholder YOUR_GDAL_VERSION below:
GDAL_DATA=/usr/share/gdal/YOUR_GDAL_VERSION
Ensure that there is no active Conda environment (except base) while building cm-vsm.
A release version of the tool can be built as follows:
cd vsm/build
cget install
cmake .. -DCMAKE_CXX_COMPILER=g++-9
make
sudo make install
Similarly, a debug version can be built as follows:
cd vsm/build
cget install
cmake .. -DCMAKE_CXX_COMPILER=g++-9 -DCMAKE_BUILD_TYPE=Debug
make
sudo make install
Once CMake has already been run in the build directory, the documentation can be built as follows:
cd vsm/build
make doc
For the program to run additional packages from the "Extensions" tab are requred:
- C/C++ (Microsoft)
- CMake Tools (Microsoft)
- CMake (twxs)
Using terminal navigate to ~/cm-vsm/vsm/build and run:
cget install
To configure the project press Ctrl + Shift + P in Visual Studio Code and run:
>CMake: Configure
If CMake cannot find CMakeLists.txt, navigate to it manually or reopen vsm folder as a project.
To build the project press Ctrl + Shift + P in Visual Studio Code and run:
>CMake: Build
To debug the project set a breakpoint, press Ctrl + Shift + P in Visual Studio Code and run:
>CMake: Debug
To avoid #include errors caused by IntelliSense, press Ctrl + Shift + P in Visual Studio Code and run:
C/C++: Edit configurations (UI)
In the configuration window specify an additional include path:
/usr/include/GraphicsMagick
and select C++17 as a C++ standard down to the bottom.
With the -d option, cm_vsm can be used to subtile a Sentinel-2 L2A raster image, for example:
./vsm/build/bin/cm_vsm -d /your/data/path/S2A_MSIL2A_20200529T094041_N0214_R036_T35VLF_20200529T120441.SAFE
The image will be processed band by band, and it might take about an hour to process the whole raster.
In order to limit processing to specific bands, the -b comma,separated,list,of,band,names argument could be used.
The results would be stored in another directory with a suffix .CVAT instead of the original .SAFE.
A CVAT annotations XML file could be rasterized with the -r option.
This is to be performed after the successful subtiling of the raster image.
Rasterization of a labelled subtile 2, 3, for example:
./vsm/build/bin/cm_vsm -r /your/data/path/S2A_MSIL2A_20200529T094041_N0214_R036_T35VLF_20200529T120441.CVAT/tile_2_3/annotations.xml -n /your/data/path/S2A_MSIL2A_20200529T094041_N0214_R036_T35VLF_20200529T120441.CVAT/tile_2_3/T35VLF_tile_2_3.nc
The following bands / layers are supported:
| No. | Name | Ground resolution | Bit-depth | Format | Description |
|---|---|---|---|---|---|
| Label | 10 m | 8 | Mask | Segmentation mask from one of the supported labeling tools | |
| 1 | TCI | 10 m | 8 | RGB | True Color Image |
| 2 | SCL | 20 m | 8 | Mask | Sen2Cor Scene Classification Image |
| 3 | AOT | 10 m | 16 | Grayscale | Aerosol Optical Thickness map |
| 4 | B01 | 60 m | 16 | Grayscale | 433 - 453 nm |
| 5 | B02 | 10 m | 16 | Grayscale | 457.5 - 522.5 nm |
| 6 | B03 | 10 m | 16 | Grayscale | 542.5 - 577.5 nm |
| 7 | B04 | 10 m | 16 | Grayscale | 650 - 680 nm |
| 8 | B05 | 20 m | 16 | Grayscale | 697.5 - 712.5 nm |
| 9 | B06 | 20 m | 16 | Grayscale | 732.5 - 747.5 nm |
| 10 | B07 | 20 m | 16 | Grayscale | 773 - 793 nm |
| 11 | B08 | 10 m | 16 | Grayscale | 784.5 - 899.5 nm |
| 12 | B8A | 20 m | 16 | Grayscale | 855 - 875 nm |
| 13 | B09 | 60 m | 16 | Grayscale | 935 - 955 nm |
| 14 | B10 | 60 m | 16 | Grayscale | 1358 - 1389 nm |
| 15 | B11 | 20 m | 16 | Grayscale | 1565 - 1655 nm |
| 16 | B12 | 20 m | 16 | Grayscale | 2100 - 2280 nm |
| 17 | WVP | 10 m | 16 | Grayscale | Water Vapour map |
| 18 | GML | Vector | GML vector mask | ||
| 19 | S2CC | 20 m | 8 | Mask | Sen2Cor cloud probabilities |
| 20 | S2CS | 20 m | 8 | Mask | Sen2Cor snow probabilities |
| 21 | FMC | 20 m | 8 | Mask | Fmask classification map |
| 22 | SS2C | 60 m | 8 | Mask | Sinergise S2Cloudless classification map |
| 23 | SS2CC | 60 m | 8 | Mask | Sinergise S2Cloudless cloud probabilities |
| 24 | MAJAC | 10 m | 8 | Mask | CNES MAJA cloud classification map |
| 25 | BHC | 60 m | 8 | Mask | Baetens & Hagolle classification map |
| 26 | FMSC | 20 m | 8 | Mask | Francis, Mrziglod, and Sidiropoulos classification map |
| 27 | GSFC | 10 m | 8 | Mask | NASA GSFC Vector mask converted into 10 m resolution raster |
| 28 | DL-L8S2-UV | 10 m | 8 | Mask | IPL-UV DL-L8S2-UV rgbiswir classification mask |