Deep Learning Models for Image Segmentation.
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