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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Abstract
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/ | ||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||
Title: | Automatically Estimating Forestal Characteristics in 3D Point Clouds using Deep Learning | ||||||||||||
Authors: |
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Date: | 29 August 2019 | ||||||||||||
Refereed publication: | Yes | ||||||||||||
Open Access: | No | ||||||||||||
Gold Open Access: | No | ||||||||||||
In SCOPUS: | No | ||||||||||||
In ISI Web of Science: | No | ||||||||||||
Status: | Accepted | ||||||||||||
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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