# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "imageseg" in publications use:' type: software license: MIT title: 'imageseg: Deep Learning Models for Image Segmentation' version: 0.5.2 doi: 10.32614/CRAN.package.imageseg abstract: A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) and the U-Net++ architecture by Zhou et al. (2018) . We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation. authors: - family-names: Niedballa given-names: Juergen email: camtrapr@gmail.com orcid: https://orcid.org/0000-0002-9187-2116 - family-names: Axtner given-names: Jan email: axtner@izw-berlin.de orcid: https://orcid.org/0000-0003-1269-5586 repository: https://cran.r-universe.dev repository-code: https://github.com/EcoDynIZW/imageseg commit: 9e418899a4d421511002b982f82074d602947277 url: https://github.com/EcoDynIZW/imageseg date-released: '2026-07-21' contact: - family-names: Niedballa given-names: Juergen email: camtrapr@gmail.com orcid: https://orcid.org/0000-0002-9187-2116