dockagile/pygile-plus repository overview

⁠Python GeoInformatics Lab Environment-Plus A headless Docker environment combining SAGA GIS, GRASS GIS, WhiteboxTools, and OTB with a complete Python geospatial stack for spatial analysis, remote sensing, and machine learning — with GPU acceleration support.

⁠What is included

⁠GIS platforms

• SAGA GIS 9.3.2
• GRASS GIS 8.4.2
• WhiteboxTools 2.4.0
• Orfeo ToolBox 9.1.1

⁠Python stack

• Core geospatial libraries: GDAL 3.12.2, PROJ 9.7.1, GEOS 3.14.1, Shapely, Fiona, Rasterio, GeoPandas
• Scientific computing: NumPy, SciPy, Pandas, Xarray
• Terrain and hydrology: RichDEM, pysheds, Landlab, TopoToolbox, xarray-spatial
• Machine learning: PyTorch, TensorFlow, scikit-learn, scikit-image
• GPU computing: CuPy, CUDA-enabled PyTorch and TensorFlow
• Visualization: Matplotlib, Plotly, Folium, Holoviews, Seaborn
• Image processing: OpenCV, scikit-image
• Web mapping: Leafmap, Geemap, ipyleaflet, localtileserver
• Cloud and remote sensing tools: Earth Engine API, STAC tools, odc-stac, Planetary Computer
• Point clouds: laspy, PDAL
• Documentation tools: Sphinx, Jupyter Book
• Jupyter Lab

⁠Core characteristics

• Headless operation
• Preconfigured environment
• Version-pinned and reproducible: every package is fixed to an exact version, and a build manifest is written into the image
• CLI and Python access to supported tools
• Docker and Apptainer workflows
• GPU-capable machine learning stack, subject to host driver compatibility

⁠Getting started

⁠Pull from Docker Hub

docker pull dockagile/pygile-plus

⁠Build from source

git clone https://github.com/Geoinformatics-Lab/PyGILE-Plus.git
cd PyGILE-Plus/docker
docker build -t pygile-plus .

The Dockerfile is self-contained; no additional files are required.

⁠Run

⁠CPU

docker run -it --name pygile-plus \
  -p 8888:8888 \
  -v $(pwd)/data:/workspace/data \
  dockagile/pygile-plus

⁠GPU

docker run -it --name pygile-plus \
  --gpus all \
  -p 8888:8888 \
  -v $(pwd)/data:/workspace/data \
  dockagile/pygile-plus

GPU use requires: - NVIDIA GPU - compatible NVIDIA driver - NVIDIA Container Toolkit on the host

Jupyter Lab starts automatically at http://localhost:8888.

Only /workspace/data is mounted in the examples above. Files written elsewhere in the container are lost when it is removed. Mount the whole /workspace directory to persist notebooks and outputs as well.

For more detailed Docker usage, see docker/README.md.

⁠Usage inside container

conda activate pygile

⁠Basic checks

python -c "import grass.script as g; print('GRASS OK')"
grass --version
saga_cmd --version
whitebox_tools --version
otbcli_BandMath -help

⁠GPU verification

python -c "import torch; print(torch.cuda.is_available())"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
python -c "import cupy; print(cupy.cuda.runtime.getDeviceCount())"

If GPU is not detected, verify host driver compatibility.

⁠Reproducibility

Every package is pinned to an exact version, and source tarballs are checksum-verified during the build. A manifest recording engine versions, compiler, checksums, library linkage, and the full resolved package list is written into the image:

docker run --rm dockagile/pygile-plus \
  cat /opt/manifest/build_manifest.txt

Also in /opt/manifest/: conda_explicit.txt (exact conda package URLs and build hashes) and pip_freeze.txt.

The environment can be verified at any time:

docker run --rm dockagile/pygile-plus pygile-verify

Deterministic numerics are enabled by default (OMP_NUM_THREADS=1 and equivalents) so floating-point results are reproducible across runs. Override at run time if throughput matters more than bit-identical output.

For citation, record the image digest:

docker inspect --format='{{index .RepoDigests 0}}' \
  dockagile/pygile-plus

⁠Known limitations

• OTB Python bindings (otbApplication) target Python 3.8 and are unavailable in this Python 3.11 environment. The OTB CLI is fully functional.
• saga_cmd returns exit code 134 on teardown despite writing correct output. When scripting SAGA, check for output files rather than return codes.
• SAGA links the system GDAL 3.0.4 while the Python stack uses GDAL 3.12.2. Raster read and write are bit-identical between the two, so values are unaffected.
• PyTorch is present as both a conda build and a CUDA wheel; the wheel takes precedence in site-packages.

⁠Tool locations

conda_env_path = "/opt/conda/envs/pygile"
saga_cmd = "/opt/saga/bin/saga_cmd"
grass_command = "grass"
otb_bin = "/opt/otb/bin"
whitebox_tools = "/opt/conda/envs/pygile/bin/whitebox_tools"
build_manifest = "/opt/manifest/build_manifest.txt"

⁠Applications

• Remote sensing analysis and classification
• Raster and vector processing
• Terrain and hydrological analysis across multiple engines
• Spatial statistics and geostatistics
• Machine learning on geospatial data
• Educational and research workflows
• Web mapping and interactive visualization
• Cloud-based geospatial analysis
• HPC workflows through Apptainer

⁠System requirements

• Docker
• Recommended memory: 8 GB minimum, more for large workloads
• Recommended storage: about 25 GB or more for the image and data
• Network access for initial pull or build
• For GPU use: NVIDIA GPU and compatible host setup
• For HPC: Apptainer or Singularity with --nv support when GPU passthrough is needed

⁠HPC and Apptainer

apptainer pull docker://dockagile/pygile-plus
apptainer run --nv pygile-plus_latest.sif

⁠License information

All included software remains under its respective upstream license. Refer to the original project documentation for specific license terms.

⁠Reference and citation

Awasthi, B., Ninsawat, S., Raghavan, V., & Nemoto, T. (2026). Geoinformatics-Lab/PyGILE-Plus: PyGILE-Plus v1.1.0 (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.18512474⁠

Awasthi, B., Ninsawat, S., Raghavan, V., & Nemoto, T. (2025). PyGILE-Plus: Python GeoInformatics Lab Environment-Plus (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.16146572⁠