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Digital Terrain Model (DTM) extraction tool

Project description

Bulldozer, a DTM extraction tool that requires only a DSM as input.

pypi docker contributions welcome license Python >=3.10 Documentation

🌏Overview

demo

Bulldozer is a pipeline designed to extract a Digital Terrain Model (DTM) from a Digital Surface Model (DSM). It supports both noisy satellite DSMs and high-quality LiDAR DSMs.

✨ Features

  • Digital Terrain Model (DTM) extraction from a single Digital Surface Model (DSM)
  • Requires only a DSM as mandatory input
  • Optional ground / non-ground mask support
  • Designed for both noisy satellite DSMs and high-quality LiDAR DSMs
  • Command-line interface (CLI)
  • Python API
  • Configuration-file based execution
  • Docker container support
  • Fully open-source (Apache 2.0)

🚀 Quick Start

🛠️ Installation

You can install Bulldozer by running the following command:

pip install bulldozer-dtm

For alternative installation methods, please refer to the documentation.

Requirements

  • Python ≥ 3.10

⚙️ Run Bulldozer

As described in the documentation, there are many different ways to launch Bulldozer. Here are the three most popular:

  1. Using the CLI (Command Line Interface) - Run the following command line after updating the parameters input_dsm.tif and output_dir:
bulldozer -dsm input_dsm.tif -out output_dir

You can also add optional parameters such as -ndsm, please refer to the helper (bulldozer -h) command to see all the options.

✅ Done! Your DTM is available in the output_dir.

  1. Using the Python API - You can directly provide the input parameters to the dsm_to_dtm function:
from bulldozer import dsm_to_dtm

dsm_to_dtm(dsm_path="input_dsm.tif", output_dir="output_dir")

✅ Done! Your DTM is available in the output_dir.

  1. Using a configuration file (CLI) - Based on provided configuration file templates, you can run the following command line:
bulldozer conf/configuration_template.yaml

✅ Done! Your DTM is available in the directory defined in the configuration file.

🐋 Bulldozer docker image

Bulldozer is available on Docker Hub and can be downloaded using:

docker pull cnes/bulldozer

And you can run Bulldozer with the following command:

Linux / macOS

docker run --user $(id -u):$(id -g) -v <absolute/path>:/data cnes/bulldozer:latest /data/<conf>.yaml

Windows (PowerShell)

docker run --rm  -v C:<absolute/path>:/data cnes/bulldozer:latest /data/<conf>.yaml

⚠️ You have to change the <absolute/path> to provide a valid absolute path to a directory where the input data are stored and where Bulldozer will write the output DTM. You also have to provide a configuration file (and rename <conf>.yaml in the command line) in this directory with an output_dir value using the data folder given to docker, e.g.: output_dir : "/data/outputdir".

✒️ Credits

If you use Bulldozer in your research, please cite the following paper:

@article{bulldozer2023,
  title={Bulldozer, a free open source scalable software for DTM extraction},
  author={Dimitri, Lallement and Pierre, Lassalle and Yannick, Ott},
  journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume = {XLVIII-4/W7-2023},
  year = {2023},
  pages = {89--94},
  url = {https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/89/2023/},
  doi = {10.5194/isprs-archives-XLVIII-4-W7-2023-89-2023}
}

📜 License

Bulldozer is licensed under permissive Apache License 2.0. Please refer to the LICENSE file for more details.

🆘 Support

For issues, questions, or feature requests, please open an issue on our GitHub Issues page or check the documentation for additional resources.

🤝Contributing

We welcome contributions! Please read our Contributing Guidelines for details on how to get involved, including coding standards and submission processes.

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Publisher: create-and-publish-wheel.yaml on CNES/bulldozer

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