TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement

2026-01-19

AI Summary

This study addresses the challenge of accurately estimating diameter at breast height (DBH) from airborne remote sensing, where distant tree trunks yield sparse imaging pixels that hinder direct measurement. To overcome this limitation, the work introduces 3D Gaussian Splatting into aerial DBH estimation for the first time, proposing a depth-aware opacity integration scheme combined with a multi-view reliability weighting strategy. These components are further integrated with a solid-circle fitting approach to strengthen geometric constraints on tree trunks under sparse observations. Evaluated across ten field plots, the method achieves a root mean square error (RMSE) of 4.79 cm (approximately 2.6 pixels), substantially outperforming a LiDAR-based baseline (7.91 cm RMSE). The results demonstrate the approach’s high accuracy and robustness in long-range, low-resolution scenarios typical of aerial imagery.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Aerial remote sensing enables efficient large-area surveying, but accurate direct object-level measurement remains difficult in complex natural scenes. Recent advancements in 3D vision, particularly learned radiance-field representations such as NeRF and 3D Gaussian Splatting, have begun to raise the ceiling on reconstruction fidelity and densifiable geometry from posed imagery. Nevertheless, direct aerial measurement of important natural attributes such as tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views: at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods leave breast-height trunk geometry weakly constrained. We present TreeDGS, an aerial image reconstruction method that leverages 3D Gaussian Splatting as a continuous, densifiable scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we estimate DBH from trunk-isolated points using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79,cm RMSE (about 2.6 pixels at this GSD) and outperforms a state-of-the-art LiDAR baseline (7.91,cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement

Problem

• aerial remote sensing
• tree diameter at breast height
• DBH measurement
• 3D reconstruction
• sparse observation

Innovation

• 3D Gaussian Splatting
• aerial DBH measurement
• opacity-weighted fitting
• dense point extraction
• remote sensing