napari-ros

Code for "Assessment and Validation of a Computer Vision Algorithm for Wildfire Rate of Spread Estimation". Documentation is WIP.

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This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

You can install napari-ros via pip: Package is not published yet.

To install latest development version :

pip install git+https://github.com/kyleawayan/napari-ros.git

Creating New Version

# the tag will be used as the version string for your package
# make it meaningful: https://semver.org/
git tag -a v0.1.0 -m "v0.1.0"

# make sure to use follow-tags so that the tag also gets pushed to github
git push --follow-tags

Then, build the package:

python -m build .

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-ros" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Citation

If you use this napari plugin or code for your research, we would appreciate a citation:

@Article{fire7120457,
  AUTHOR = {Ameri, Ehsan and Awayan, Kyle and Cobian-Iñiguez, Jeanette},
  TITLE = {Assessment and Validation of a Computer Vision Algorithm for Wildfire Rate of Spread Estimation},
  JOURNAL = {Fire},
  VOLUME = {7},
  YEAR = {2024},
  NUMBER = {12},
  ARTICLE-NUMBER = {457},
  URL = {https://www.mdpi.com/2571-6255/7/12/457},
  ISSN = {2571-6255},
  ABSTRACT = {As wildfire activity increases worldwide, developing effective methods for estimating how fast it can spread is critical. This study aimed to develop and validate a computer vision algorithm for fire spread estimation. Using visual flame data from laboratory experiments on excelsior and pine needle fuel beds, we explored fire spread predictions for two types of experiments. In the first, the experiments were conducted in an environment where the flame was maintained visually undisturbed while in the second, real-world scenarios were simulated with visual obstructions. Algorithm performance evaluation was conducted by computing the index of agreement and normalized root mean square deviation (NRMSD) error. Results show that the algorithm estimates fire spread well in pristine visual environments with varying accuracy depending on the fuel type. For instance, the index of agreement between the rate of spread values estimated by the algorithm and the measured values is 0.56 for excelsior fuel beds and 0.51 for pine needle fuel beds. For visual obstructions, varying impacts on the rate of spread predictions were observed. Adding an orange background behind the flame had the least effect on algorithm performance (IAmedian = 0.45), followed by placing a Y-shape element resembling a branch (IAmedian = 0.31) and adding an LED light near the flame (IAmedian = 0.30).},
  DOI = {10.3390/fire7120457}
}