Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks

Johannes Leonhardt, Lukas Drees, Peter Jung, Ribana Roscher

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Biomass is an important variable for our understanding of the terrestrial carbon cycle, facilitating the need for satellite-based global and continuous monitoring. However, current machine learning methods used to map biomass can often not model the complex relationship between biomass and satellite observations or cannot account for the estimation’s uncertainty. In this work, we exploit the stochastic properties of Conditional Generative Adversarial Networks for quantifying aleatoric uncertainty. Furthermore, we use generator Snapshot Ensembles in the context of epistemic uncertainty and show that unlabeled data can easily be incorporated into the training process. The methodology is tested on a newly presented dataset for satellite-based estimation of biomass from multispectral and radar imagery, using lidar-derived maps as reference data. The experiments show that the final network ensemble captures the dataset’s probabilistic characteristics, delivering accurate estimates and well-calibrated uncertainties.

Original languageEnglish
Title of host publicationPattern Recognition - 44th DAGM German Conference, DAGM GCPR 2022, Proceedings
EditorsBjörn Andres, Florian Bernard, Daniel Cremers, Simone Frintrop, Bastian Goldlücke, Ivo Ihrke
PublisherSpringer Science and Business Media Deutschland GmbH
Pages479-494
Number of pages16
ISBN (Print)9783031167874
DOIs
StatePublished - 2022
Event44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022 - Konstanz, Germany
Duration: 27 Sep 202230 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13485 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022
Country/TerritoryGermany
CityKonstanz
Period27/09/2230/09/22

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