Awesome
ALUS
Collection of processing operators/routines defined by software - plural of ALU(Arithmetic Logic Unit)
or
ALU for Space/Surveillance etc.
A software project that targets to utilize Nvidia GPUs for processing earth observation data (faster).
Kickstart of this project was funded
through ESA's EOEP programme
Current development is funded
through ESA's GSTP programme
Developed by CGI Estonia.
Quick performance overview
For further comprehensive evaluation see Wiki
Out of the box usage
Verified releases can be downloaded from - https://github.com/cgi-estonia-space/ALUs/releases/
One can download docker image with all of the needed dependencies
from dockerhub or simply docker pull cgialus/alus-devel
Executing
Each algorithm is a separate executable. Currently available ones are (more info and usage in parenthesis):
- Sentinel 1 coherence estimation routine -
alus-coh(README) - Sentinel 1 coherence estimation timeline generation -
alus-coht(README) - Sentinel 1 calibration routine -
alus-cal(README) - Sentinel 2 and other raster resample and tiling -
alus-resa(README) - Gabor feature extraction -
alus-gfe(README) - PALSAR level 0 focuser -
alus-palsar-focus(README)
When building separately, these are located at <build_dir>/alus_package
Update PATH environment variable in order to execute everywhere:
export PATH=$PATH:/path/to/<alus_package>
See --help for specific arguments/parameters how to invoke processing. For more information see detailed explanation
of Sentinel 1 processors' processing arguments.
Docker example
NVIDIA driver and NVIDIA Container Toolkit must be installed together with docker.
docker pull cgialus/alus-devel:latest
docker run -t -d --gpus all --name alus_container cgialus/alus-devel
docker exec -t alus_container mkdir /root/alus
docker cp <latest build tar archive> alus_container:/root/alus/
docker exec -t alus_container bash -c "tar -xzf /root/alus/*.tar.gz -C /root/alus/"
# Use docker cp to transfer all the input datasets, auxiliary data, then either
docker exec -t alus_container bash -c "cd /root/alus; ./alus-<alg> ...."
# Or connect to shell
docker exec -it alus_container /bin/bash
# Running coherence estimation routine example
./alus-coh -r S1A_IW_SLC__1SDV_20200724T034334_20200724T034401_033591_03E49D_96AA.SAFE \
-s S1A_IW_SLC__1SDV_20200805T034334_20200805T034401_033766_03E9F9_52F6.SAFE \
-o /tmp/ -p VV --orbit_dir <orbit files location> --sw IW1 --dem srtm_43_06.tif --dem srtm_44_06.tif
# Running calibration routine example
./alus-cal -i S1A_IW_SLC__1SDV_20180815T154813_20180815T154840_023259_028747_4563.SAFE \
-o /tmp/alus_S1A_IW_SLC__1SDV_20180815T154813_20180815T154840_023259_028747_4563_Calib_tc.tif \
--sw IW1 -p VV --type beta --dem srtm_42_01.tif
Dependencies
Building
cmake . -Bbuild
cd build
make -j8
Jupyter Notebook
There is a Jupyter Notebook located at jupyter-notebook folder with a user-friendly interface and automated auxiliary file downloads.
It can be used in conjunction with binaries to easily execute code. Read the instructions.
Minimum/Recommended requirements
For specific, check each processor's README.
Below are rough figures:
- NVIDIA GPU device compute capability 6.0 (Pascal) or higher
- 2(minimum)/4(recommended) GB of available device memory (some ALUs can manage with less)
- High speed (NVMe) SSD to benefit from the computation speedups
- 4 GB of extra RAM to enable better caching/input-output (GDAL raster IO)