Metadata-Version: 2.1
Name: detectree
Version: 0.4.0
Summary: Tree detection from aerial imagery
Home-page: https://github.com/martibosch/detectree
Author: Martí Bosch
Author-email: marti.bosch@epfl.ch
License: GPL-3.0
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        # DetecTree
        
        ## Overview
        
        DetecTree is a Pythonic library to classify tree/non-tree pixels from aerial imagery, following the methods of Yang et al. [1]. The target audience is researchers and practitioners in GIS that are interested in two-dimensional aspects of trees, such as their proportional abundance and spatial distribution throughout a region of study. These measurements can be used to assess important aspects of urban planning such as the provision of urban ecosystem services. The approach is of special relevance when LIDAR data is not available or it is too costly in monetary or computational terms.
        
        ```python
        import detectree as dtr
        import matplotlib.pyplot as plt
        import rasterio as rio
        from rasterio import plot
        
        # select the training tiles from the tiled aerial imagery dataset
        ts = dtr.TrainingSelector(img_dir='data/tiles')
        split_df = ts.train_test_split(method='cluster-I')
        
        # train a tree/non-tree pixel classfier
        clf = dtr.ClassifierTrainer().train_classifier(
            split_df=split_df, response_img_dir='data/response_tiles')
            
        # use the trained classifier to predict the tree/non-tree pixels
        test_filepath = split_df[~split_df['train'].sample(1).iloc[0]['img_filepath']
        y_pred = dtr.Classifier().classify_img(test_filepath, clf)
        
        # side-by-side plot of the tile and the predicted tree/non-tree pixels
        figwidth, figheight = plt.rcParams['figure.figsize']
        fig, axes = plt.subplots(1, 2, figsize=(2 * figwidth, figheight))
        
        with rio.open(img_filepath) as src:
            plot.show(src.read(), ax=axes[0])
        axes[1].imshow(y_pred)
        ```
        
        ![Example](figures/example.png)
        
        A full example application of DetecTree to predict a tree canopy map for the Aussersihl district in Zurich [is available as a Jupyter notebook](https://github.com/martibosch/detectree-example/blob/master/notebooks/aussersihl-canopy.ipynb). See also [the API reference documentation](https://detectree.readthedocs.io/en/latest/?badge=latest) and the [example repository](https://github.com/martibosch/detectree-example) for more information on the background and some example notebooks.
        
        ## Citation
        
        Bosch M. 2020. “DetecTree: Tree detection from aerial imagery in Python”. *Journal of Open Source Software, 5(50), 2172.* [doi.org/10.21105/joss.02172](https://doi.org/10.21105/joss.02172)
        
        Note that DetecTree is based on the methods of Yang et al. [1], therefore it seems fair to reference their work too. An example citation in an academic paper might read as follows:
        
        > The classification of tree pixels has been performed with the Python library DetecTree (Bosch, 2020), which is based on the approach of Yang et al. (2009).
        
        ## Installation
        
        To install use pip:
        
            $ pip install detectree
        
        ## See also
        
        * [lausanne-tree-canopy](https://github.com/martibosch/lausanne-tree-canopy): example computational workflow to get the tree canopy of Lausanne with DetecTree
        * [A video of a talk about DetecTree](https://www.youtube.com/watch?v=USwF2KyxVjY) in the [Applied Machine Learning Days of EPFL (2020)](https://appliedmldays.org/) and [its respective slides](https://martibosch.github.io/detectree-amld-2020)
        
        ## Acknowledgments
        
        * With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
        
        
        ## References
        
        1. Yang, L., Wu, X., Praun, E., & Ma, X. (2009). Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 131-137). ACM.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: laszip
