imageseg

R package for deep learning image segmentation using the U-Net model architecture by Ronneberger (2015), implemented in Keras and TensorFlow. It provides pre-trained models for forest structural metrics (canopy density and understory vegetation density) and a workflow to apply these on custom images.

In addition, it provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on the U-net architecture. Model can be trained on grayscale or color images, and can provide binary or multi-class image segmentation as output.

Installation

Install the package via:

library(remotes)
install_github("EcoDynIZW/imageseg", build_vignettes = TRUE)

See the vignette for information about setting up the Keras and TensorFlow.

Where are the models?

The pre-trained models for forest canopy density and understory vegetation density are available for download:

Canopy model: https://www.dropbox.com/s/rtsly7kfag9fzlh/imageseg_canopy_model.hdf5?dl=1

Understory model: https://www.dropbox.com/s/9qvgcc9j5r36spp/imageseg_understory_model.hdf5?dl=1

Please see the vignette for further information.

Example classifications to give you an impression of model performance:

Canopy model examples https://www.dropbox.com/sh/ypxx5rknpgqolxk/AAATyhQ8-wIi5I9aGlekqn7ia?dl=0

Understory model examples https://www.dropbox.com/sh/4gngdvk7km92clp/AAC2EtoB7lZiQefWVIwFiWZha?dl=0

Training data download

Canopy training data https://www.dropbox.com/s/302yyoi7qil1hn5/canopy_training_data_imageseg.zip?dl=1

Understory training data https://www.dropbox.com/s/s7o7x66l3wiqc6h/understory_training_data_imageseg.zip?dl=1