{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/three-dimensional-segmentation-of-trees","title":"Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)","arxiv_id":"1903.08481","date":"2019-03-20","proceeding":null,"authors":["Jonathan Williams","Carola-Bibiane Schönlieb","Tom Swinfield","Juheon Lee","Xiaohao Cai","Lan Qie","David A. Coomes"],"abstract":"Developing a robust algorithm for automatic individual tree crown (ITC)\ndetection from laser scanning datasets is important for tracking the responses\nof trees to anthropogenic change. Such approaches allow the size, growth and\nmortality of individual trees to be measured, enabling forest carbon stocks and\ndynamics to be tracked and understood. Many algorithms exist for structurally\nsimple forests including coniferous forests and plantations. Finding a robust\nsolution for structurally complex, species-rich tropical forests remains a\nchallenge; existing segmentation algorithms often perform less well than simple\narea-based approaches when estimating plot-level biomass. Here we describe a\nMulti-Class Graph Cut (MCGC) approach to tree crown delineation. This uses\nlocal three-dimensional geometry and density information, alongside knowledge\nof crown allometries, to segment individual tree crowns from LiDAR point\nclouds. Our approach robustly identifies trees in the top and intermediate\nlayers of the canopy, but cannot recognise small trees. From these\nthree-dimensional crowns, we are able to measure individual tree biomass.\nComparing these estimates to those from permanent inventory plots, our\nalgorithm is able to produce robust estimates of hectare-scale carbon density,\ndemonstrating the power of ITC approaches in monitoring forests. The\nflexibility of our method to add additional dimensions of information, such as\nspectral reflectance, make this approach an obvious avenue for future\ndevelopment and extension to other sources of three-dimensional data, such as\nstructure from motion datasets.","url_abs":"http://arxiv.org/abs/1903.08481v1","url_pdf":"http://arxiv.org/pdf/1903.08481v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"three-dimensional-segmentation-of-trees","repo_url":"https://github.com/jonvw28/MCGC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}