Tree Counting by Bridging 3D Point Clouds with Imagery

TL;DR

This paper introduces FuseCountNet, a deep learning method that combines 3D LiDAR data and 2D imagery to improve the accuracy of tree counting in forests, outperforming existing algorithms.

Contribution

The paper presents a novel deep learning approach that fuses 3D LiDAR and 2D imagery for more accurate tree counting, establishing a new benchmark with the NeonTreeCount dataset.

Findings

FuseCountNet outperforms state-of-the-art methods in tree counting accuracy.

Fusing 3D and 2D data improves differentiation of individual trees.

Empirical evaluation confirms the effectiveness of the proposed approach.

Abstract

Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy and does not allow for easy separation of trees when the canopy is dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees. We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery. The approach is compared with state-of-the-art algorithms, like operating on 3D point cloud and 2D imagery. We empirically evaluate the different methods on the NeonTreeCount data set, which we use to define a tree-counting benchmark. The experiments show that FuseCountNet yields more accurate tree counts.