Deep learning based 3D point cloud regression for estimating forest biomass
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Deep learning based 3D point cloud regression for estimating forest biomass. / Oehmcke, Stefan; Li, Lei; Revenga, Jaime C.; Nord-Larsen, Thomas; Trepekli, Katerina; Gieseke, Fabian; Igel, Christian.
30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022. ed. / Matthias Renz; Mohamed Sarwat; Mario A. Nascimento; Shashi Shekhar; Xing Xie. Association for Computing Machinery, Inc., 2022. p. 1-4 38.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Deep learning based 3D point cloud regression for estimating forest biomass
AU - Oehmcke, Stefan
AU - Li, Lei
AU - Revenga, Jaime C.
AU - Nord-Larsen, Thomas
AU - Trepekli, Katerina
AU - Gieseke, Fabian
AU - Igel, Christian
N1 - Publisher Copyright: © 2022 Owner/Author.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. Specifically, we devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in a national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, no digital terrain model is required. We expect this finding to have a strong impact on LiDAR-based analyses of terrestrial ecosystem dynamics.
AB - Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. Specifically, we devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in a national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, no digital terrain model is required. We expect this finding to have a strong impact on LiDAR-based analyses of terrestrial ecosystem dynamics.
KW - biomass
KW - climate change
KW - datasets
KW - LiDAR
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85143591559&partnerID=8YFLogxK
U2 - 10.1145/3557915.3561471
DO - 10.1145/3557915.3561471
M3 - Article in proceedings
AN - SCOPUS:85143591559
SP - 1
EP - 4
BT - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
A2 - Renz, Matthias
A2 - Sarwat, Mohamed
A2 - Nascimento, Mario A.
A2 - Shekhar, Shashi
A2 - Xie, Xing
PB - Association for Computing Machinery, Inc.
T2 - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
Y2 - 1 November 2022 through 4 November 2022
ER -
ID: 337982106