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Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning

Citation

Krisanski, S and Taskhiri, MS and Aracil, SG and Herries, D and Turner, P, Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning, Remote Sensing, 13, (8) Article 1413. ISSN 2072-4292 (2021) [Refereed Article]


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Copyright Statement

Copyright 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/rs13081413

Abstract

Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.

Item Details

Item Type:Refereed Article
Keywords:deep learning, segmentation, forest, point cloud, LiDAR, photogrammetry, terrestrial laser scanning structure from motion, automated, digital terrain model
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Plant Production and Plant Primary Products
Objective Group:Forestry
Objective Field:Softwood plantations
UTAS Author:Krisanski, S (Mr Sean Krisanski)
UTAS Author:Taskhiri, MS (Dr Mohammad Sadegh Taskhiri)
UTAS Author:Turner, P (Associate Professor Paul Turner)
ID Code:144324
Year Published:2021
Deposited By:Sustainable Marine Research Collaboration
Deposited On:2021-05-13
Last Modified:2021-06-23
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