Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass

Oehmcke, Stefan; Li, Lei; Revenga, Jaime; Nord-Larsen, Thomas; Trepekli, Katerina; Gieseke, Fabian; Igel, Christian


Zusammenfassung

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.

Schlüsselwörter
point cloud data; biomass estimation; deep learning



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2022

Konferenz
International Conference on Advances in Geographic Information Systems (SIGSPATIAL)

Konferenzort
Seattle, Washington

Buchtitel
30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022)

Herausgeber
Renz, Matthias; Sarwat, Mohamed

Erste Seite
1

Letzte Seite
4

Verlag
ACM Press

Ort
New York, NY, USA

Sprache
Englisch

DOI