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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.
Health stats visible at Monitor.