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

2025-11-28

AI Summary

Traditional methods struggle to accurately and cost-effectively estimate aboveground biomass (AGB) in dense forests. To address this, we propose the first end-to-end deep learning framework for dense AGB prediction from a single ground-level RGB image. Our key innovation is formulating pixel-wise AGB density maps as the regression target. We synthesize a large-scale 3D forest dataset—SPREAD—by leveraging instance segmentation masks and tree attributes (e.g., species, diameter at breast height) to generate pixel-level AGB ground truth via allometric equations. Critically, our method requires neither LiDAR nor multi-view imagery, greatly enhancing scalability and enabling broad public participation. Experiments demonstrate strong generalization: median absolute error is 1.22 kg/m² on the held-out SPREAD test set and 1.94 kg/m² on real-world forest images, confirming both accuracy and practical applicability.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Remote Sensing / Geospatial AI
• Multiagent Systems: Distributed Problem Solving

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Forests 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 1.22 kg/m^2 on held-out SPREAD data and 1.94 kg/m^2 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.

Problem

• Estimating aboveground biomass from single RGB images
• Overcoming limitations of traditional forest measurement methods
• Developing scalable biomass monitoring using synthetic training data

Innovation

• Single RGB image biomass estimation via deep learning
• Synthetic 3D forest scenes with allometric equations
• Dense prediction framework using AGB density maps