Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction

2026-05-11

AI Summary

This study addresses the limitations of traditional forest fuel load estimation, which relies on costly airborne LiDAR or ground surveys, while satellite imagery lacks vertical resolution for accurate canopy structure reconstruction. The authors propose a low-cost, near-real-time alternative that leverages Google Earth Studio to generate synthetic remote sensing imagery and camera poses, combined with the Pi-Long feedforward Transformer for dense 3D reconstruction. Metric scale recovery is achieved through Sim(3) Umeyama alignment. By analyzing canopy height and density features from bird’s-eye-view representations, the method integrates watershed segmentation and height variance analysis to enable tree species classification, leaf area index estimation, and fuel load prediction. This work presents the first integration of virtual remote sensing with scale-aware feedforward 3D reconstruction, significantly enhancing geometric consistency and scalability for efficient wildfire risk assessment.

Abstract

Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. However, traditional methods relying on airborne LiDAR or field surveys are cost-prohibitive and time-intensive, while satellite imagery often lacks the vertical resolution required for canopy volume analysis. This paper proposes a novel, automated pipeline for rapid forest inventory using virtual remote sensing data derived from Google Earth Studio (GES). Our approach first generates low-altitude orbital imagery and camera poses for a target region. For dense 3D reconstruction, we employ Pi-Long, developed within the VGGT-Long framework. This model serves as a scalable extension of the Pi-3 feed-forward Transformer architecture. To address the inherent scale ambiguity in monocular reconstruction, we introduce a metric recovery module that aligns the reconstructed trajectory with GES ground truth poses via Sim(3) Umeyama optimization. The metric-scale point cloud is then orthogonally projected into Bird's-Eye-View (BEV) height and density maps. Finally, we employ a watershed-based segmentation algorithm combined with height variance analysis to classify tree species (conifer vs. broadleaf), calculate Leaf Area Index (LAI), and estimate total fuel load. Experimental results demonstrate that this pipeline offers a scalable, cost-effective alternative to physical scanning, enabling near-real-time estimation of forest biomass with high geometric consistency.

Problem

• forest fuel load
• wildfire risk assessment
• 3D reconstruction
• remote sensing
• biomass estimation

Innovation

• virtual remote sensing
• metric-scale 3D reconstruction
• feed-forward Transformer
• Sim(3) alignment
• fuel load estimation