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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.

The package can be found on CRAN:

https://cran.r-project.org/web/packages/imageseg/index.html

The preprint of the paper describing the package is available on bioRxiv:

https://doi.org/10.1101/2021.12.16.469125

Installation

First, install the R package “R.rsp” which enables the static vignettes.

install.packages(R.rsp)

Install the imageseg package from CRAN via:

install.packages(imageseg)

Alternatively you can install from GitHub (requires remotes package and R.rsp):

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

Using imageseg requires Keras and TensorFlow. See the vignette for information about installation and initial setup:

Tutorial

See the vignette for an introduction and tutorial to imageseg.

browseVignettes("imageseg")

The vignette covers:

• Installation and setup
• Sample workflow for canopy density assessments
• Training new models
• Continued training of existing models
• Multi-class image segmentation models
• Image segmentation based on grayscale images

Forest structure model download

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.