semvec/pypi-packages huggingface dataset:
canopy-shade 0.0.3
Python package to calculate the shading effect of tree canopies from panoramic imagery
License: MIT license

============
Canopy Shade
============

.. image:: https://img.shields.io/pypi/v/canopy_shade.svg
        :target: https://pypi.python.org/pypi/canopy_shade
        :alt: PyPI Version

.. image:: https://colab.research.google.com/assets/colab-badge.svg
   :target: https://colab.research.google.com/drive/1fUcqN6aSLGZnzzahIZiy_AkigFn5gY2e?usp=sharing
   :alt: Open In Colab

Python package to calculate the shading effect of tree canopies from panoramic imagery

* Free software: MIT license
* Documentation: https://canopy-shade.readthedocs.io.

Features
--------

* TODO

Citation
--------

Please cite the paper_ if you use `Canopy Shade Mapper` in a scientific publication:

.. _paper: https://doi.org/

.. code-block:: none

   Fujiwara, K., Khomiakov, M., Yap, W., Ignatius, M., & Biljecki, F. (2024). Microclimate Vision: Multimodal prediction of climatic parameters using street-level and satellite imagery. Sustainable Cities and Society, 105733. doi:10.1016/j.scs.2024.105733

.. code-block:: bibtex

   @article{XXX,
     title={XXX},
     author={XXX, XXX, XXX, XXX, ...},
     journal={XXX},
     volume={XXX},
     pages={XXX},
     year={2024}
   }

.. code-block:: bibtex

   @article{ito2024zensvi,
     title={ZenSVI: One-Stop Python Package for Integrated Analysis of Street View Imagery},
     author={Ito, Koichi, XXX, XXX, XXX, ...},
     journal={XXX},
     volume={XXX},
     pages={XXX},
     year={2024}
   }

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage