Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes

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dc.contributor.authorZuffi, Silviaen_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:13Z
dc.date.available2025-11-21T07:28:13Z
dc.date.issued2025
dc.description.abstractForests play a critical role in global ecosystems by supporting biodiversity and mitigating climate change via carbon sequestration. Accurate aboveground biomass (AGB) estimation is essential for assessing carbon storage and wildfire fuel loads, yet traditional methods rely on labor-intensive field measurements or remote sensing approaches with significant limitations in dense vegetation. In this work, we propose a novel learning-based method for estimating AGB from a single ground-based RGB image. We frame this as a dense prediction task, introducing AGB density maps, where each pixel represents tree biomass normalized by the plot area and each tree's image area. We leverage the recently introduced synthetic 3D SPREAD dataset, which provides realistic forest scenes with per-image tree attributes (height, trunk and canopy diameter) and instance segmentation masks. Using these assets, we compute AGB via allometric equations and train a model to predict AGB density maps, integrating them to recover the AGB estimate for the captured scene. Our approach achieves a median AGB estimation error of 1:22kg=m2 on held-out SPREAD data and 1:94kg=m2 on a real-image dataset. To our knowledge, this is the first method to estimate aboveground biomass directly from a single RGB image, opening up the possibility for a scalable, interpretable, and cost-effective solution for forest monitoring, while also enabling broader participation through citizen science initiatives.en_US
dc.description.sectionheadersLearning-based Algorithms
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251328
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251328
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251328
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleLearning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenesen_US
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