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Automatically Estimating Forestal Characteristics in 3D Point Clouds using Deep Learning

Contreras, Jhonatan and Denzler, Joachim and Sickert, Sven (2019) Automatically Estimating Forestal Characteristics in 3D Point Clouds using Deep Learning. iDiv Annual Conference 2019, 29-30 August 2019, Leipzig, Germany.

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Biodiversity changes can be monitored using georeferenced and multitempo-ral data. Those changes refer to the process of automatically identifying differ-ences in the measurements computed over time. The height and the Diameterat Breast Height of the trees can be measured at different times. The mea-surements of individual trees can be tracked over the time resulting in growthrates, tree survival, among other possibles applications. We propose a deeplearning-based framework for semantic segmentation, which can manage largepoint clouds of forest areas with high spatial resolution. Our method divides apoint cloud into geometrically homogeneous segments. Then, a global feature isobtained from each segment, applying a deep learning network called PointNet.Finally, the local information of the adjacent segments is included through anadditional sub-network which applies edge convolutions. We successfully trainand test in a data set which covers an area with multiple trees. Two addi-tional forest areas were also tested. The semantic segmentation accuracy wastested using F1-score for four semantic classes:leaves(F1 = 0.908),terrain(F1 = 0.921),trunk(F1 = 0.848) anddead wood(F1 = 0.835). Furthermore,we show how our framework can be extended to deal with forest measurementssuch as measuring the height of the trees and the DBH.

Item URL in elib:https://elib.dlr.de/133241/
Document Type:Conference or Workshop Item (Poster)
Title:Automatically Estimating Forestal Characteristics in 3D Point Clouds using Deep Learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Contreras, JhonatanUNSPECIFIEDhttps://orcid.org/0000-0002-0491-9896UNSPECIFIED
Denzler, JoachimFSU Jenahttps://orcid.org/0000-0002-3193-3300UNSPECIFIED
Date:29 August 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Semantic Segmentation, Point Cloud, Deep Learning, Change Detection.
Event Title:iDiv Annual Conference 2019
Event Location:Leipzig, Germany
Event Type:Workshop
Event Dates:29-30 August 2019
Organizer:German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Contreras, Jhonatan
Deposited On:23 Jan 2020 15:52
Last Modified:23 Jan 2020 15:52

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