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DetecTree is a Pythonic library to perform semantic segmentation of aerial imagery into tree/non-tree pixels, following the methods of Yang et al. [1]. A pre-trained model is available atHugging Face hub, which can be used as follows:

fromurllibimportrequestimportdetectreeasdtrimportmatplotlib.pyplotaspltimportrasterioasriofromrasterioimportplot# download a tile from the SWISSIMAGE WMStile_url= ("https://wms.geo.admin.ch/?SERVICE=WMS&REQUEST=GetMap&VERSION=1.3.0&""FORMAT=image/png&LAYERS=ch.swisstopo.images-swissimage&CRS=EPSG:2056""&BBOX=2532980,1152150,2533380,1152450&WIDTH=800&HEIGHT=600")tile_filename="tile.png"request.urlretrieve(tile_url,tile_filename)# use the pre-trained model to segment the image into tree/non-tree-pixelsy_pred=dtr.Classifier().predict_img(tile_filename)# side-by-side plot of the tile and the predicted tree/non-tree pixelsfigwidth,figheight=plt.rcParams["figure.figsize"]fig,axes=plt.subplots(1,2,figsize=(2*figwidth,figheight))withrio.open(tile_filename)assrc:plot.show(src,ax=axes[0])axes[1].imshow(y_pred)

Alternatively, you can use detectree to train your own model on your aerial imagery dataset:

importdetectreeasdtrimportmatplotlib.pyplotaspltimportrasterioasriofromrasterioimportplot# select the training tiles from the tiled aerial imagery datasetts=dtr.TrainingSelector(img_dir='data/tiles')split_df=ts.train_test_split(method='cluster-I')# train a tree/non-tree pixel classifierclf=dtr.ClassifierTrainer().train_classifier(split_df=split_df,response_img_dir='data/response_tiles')# use the trained classifier to predict the tree/non-tree pixelstest_filepath=split_df[~split_df['train'].sample(1).iloc[0]['img_filepath']y_pred=dtr.Classifier(clf=clf).classify_img(test_filepath)# side-by-side plot of the tile and the predicted tree/non-tree pixelsfigwidth,figheight=plt.rcParams['figure.figsize']fig,axes=plt.subplots(1,2,figsize=(2*figwidth,figheight))withrio.open(img_filepath)assrc:plot.show(src.read(),ax=axes[0])axes[1].imshow(y_pred)

A full example application of DetecTree to predict a tree canopy map for the Aussersihl district in Zurichis available as a Jupyter notebook. See alsothe API reference documentation and theexamples repository for more information on the background and some example notebooks.

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.

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

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).

The easiest way to installdetectree is with conda as in:

conda install -c conda-forge detectree

You can installdetectree with pip as in:

pip install detectree

If you want to be able to read compressed LAZ files, you will needthe Python bindings forlaszip. Note that the latter require [laszip], which can be installed using conda (which is automatically handled when installingdetectree with conda as shown above) or downloaded fromlaszip.org. Then, detectree and the Python bindings forlaszip can be installed with pip as in:

pip install detectree[laszip]

To install a development version of detectree, you can first use conda to create an environment with all the dependencies - with theenvironment-dev.yml file - and activate it as in:

conda env create -f environment-dev.ymlconda activate detectree-dev

and then clone the repository and use pip to install it in development mode

git clone git@github.com:martibosch/detectree.gitcd detectree/pip install -e.

This will also install the dependencies required for running tests, linting the code and building the documentation. Additionally, you can activatepre-commit so that the latter are run as pre-commit hooks as in:

pre-commit install

• lausanne-tree-canopy: example computational workflow to get the tree canopy of Lausanne with DetecTree
• A video of a talk about DetecTree in theApplied Machine Learning Days of EPFL (2020) andits respective slides

• With the support of the École Polytechnique Fédérale de Lausanne (EPFL)

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.