DOI: 10.5061/dryad.x0k6djhnj
title: Data from: imageseg: An R package for deep learning-based image segmentation

description: 1. Convolutional neural networks (CNNs) and deep learning are powerful and
 robust tools for ecological applications, and are particularly suited for
 image data. Image segmentation (the classification of all pixels in
 images) is one such application and can for example be used to assess
 forest structural metrics. While CNN-based image segmentation methods for
 such applications have been suggested, widespread adoption in ecological
 research has been slow, likely due to technical difficulties in
 implementation of CNNs and lack of toolboxes for ecologists. 2. Here, we
 present R package imageseg which implements a CNN-based workflow for
 general-purpose image segmentation using the U-Net and U-Net++
 architectures in R. The workflow covers data (pre)processing, model
 training, and predictions. We illustrate the utility of the package with
 image recognition models for two forest structural metrics: tree canopy
 density and understory vegetation density. We trained the models using
 large and diverse training data sets from a variety of forest types and
 biomes, consisting of 2877 canopy images (both canopy cover and
 hemispherical canopy closure photographs) and 1285 understory vegetation
 images. 3. Overall segmentation accuracy of the models was high with a
 Dice score of 0.91 for the canopy model and 0.89 for the understory
 vegetation model (assessed with 821 and 367 images, respectively). The
 image segmentation models performed significantly better than commonly
 used thresholding methods, and generalized well to data from study areas
 not included in training. This indicates robustness to variation in input
 images and good generalization strength across forest types and biomes. 4.
 The package and its workflow allow simple yet powerful assessments of
 forest structural metrics using pre-trained models. Furthermore, the
 package facilitates custom image segmentation with single or multiple
 classes and based on color or grayscale images, e.g. for applications in
 cell biology or for medical images. Our package is free, open source, and
 available from CRAN. It will enable easier and faster implementation of
 deep learning-based image segmentation within R for ecological
 applications and beyond.
description: This data set contains training data, R scripts and pre-trained
 models for two image segmentation models for forest structural metrics:
 canopy density and understory vegetation density. Models were implemented
 in R via Keras using a TensorFlow backend.
 <em>imageseg_canopy_model.zip</em>
 Contains model file (.hdf5) and "examples" folder for
 canopy density image segmentation model. In the examples, left column is
 model input, central column is raw model output, right column is binarized
 output. <em>imageseg_canopy_training_data.zip
 </em> Training data used for canopy density model
 (masks and images, 256x256 pixels), split into three folders. See the
 info.txt file for details.
 <em>imageseg_canopy_training_run.R </em>
 R script used for canopy model training.
 <em>imageseg_understory_model.zip</em>
 Contains model file (.hdf5) and "examples" folder for
 understory vegetation density image segmentation model. In the examples,
 left column is model input, central column is raw model output, right
 column is binarized output.
 <em>imageseg_understory_training_data.zip
 </em> Training data used for understory
 vegetation density model (masks and images, 160x256 pixels).
 <em>imageseg_understory_training_run.R</em>
 R script used for understory vegetation model training.