Skip to main content

Tree detection from aerial imagery

Project description

PyPI version fury.io Documentation Status Build Status Coverage Status GitHub license

DetecTree

Overview

DetecTree is a Pythonic library to classify tree/non-tree pixels from aerial imagery, following the methods of Yang et al. [1].

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

See the API documentation and the example repository to get started.

Installation

To install use pip:

$ pip install detectree

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for detectree, version 0.3.1
Filename, size File type Python version Upload date Hashes
Filename, size detectree-0.3.1.tar.gz (32.4 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page