We introduceTreeStructor, a novel approach for isolating and reconstructing forest trees. The key novelty is a deep neural model that uses neural ranking to assign pre-generated connectable 3D geometries to a point cloud.TreeStructoris trained on a large set of synthetically generated point clouds. The input to our method is a forest point cloud (FPC) that we first decompose into point clouds that approximately represent trees (TPC) and then into point clouds that represent their parts (PPC). We use a point cloud encoder-decoder to compute embedding vectors that retrieve the best-fitting surface mesh for eachPPCfrom a large set of predefined branch parts. Finally, the retrieved meshes are connected and oriented to obtain individual surface meshes of all trees represented by theFPC. We qualitatively and quantitatively validate that our method can reconstruct forest trees with unprecedented accuracy and visual fidelity.TreeStructoroutperforms the state-of-the-art reconstruction method for around 6% on quantitative metrics and 12% less error compared with QSM on low-quality scanned data. The code and data are available at https://lewkesy.github.io/TreeStructor/.

Presents corrections to the paper, (Errata to “TreeStructor: Forest Reconstruction With Neural Ranking”).

Image segmentation using deep learning has become overwhelmingly widespread. However, routine model testing methods can encounter evaluation inconsistencies or bias, largely due to how accuracy metrics respond to variations in class share distribution. Here, we address the effects of class imbalance on model performance evaluation and demonstrate a refined approach that incorporates image classification efficacy (ICE) metrics within the context of semantic segmentation in remote sensing. This evaluation approach was applied in six segmentation experiments that involved multispectral and LiDAR data, single or multiple models tested with the same or different datasets, and binary and multiclass schemes. ICE metrics revealed unique aspects of model’s segmentation capabilities compared to precision, recall, F-score, and overall accuracy. By mitigating the class imbalance effect, per-class efficacy enables precise class-level optimization of segmentation models, while whole-class efficacy facilitates evaluating a model’s potential performance when adapted to new datasets. The suitability of the kappa coefficient, ROC-AUC, and PR-AUC for model evaluation under class imbalance was discussed in comparison with ICE metrics. This efficacy-enhanced model evaluation protocol can be implemented for deep learning model training and testing. The routine use of this evaluation approach will strengthen the dependability and applicability of segmentation tools in various fields.

Segmenting major woody parts is a critical prerequisite to derive structural and biophysical attributes of trees. Static Terrestrial laser scanning (TLS) has been widely used due to its accurate and non-destructive scanning capability; wood parts segmentation has been experimented using the raw radiometric feature. However, due to the challenges of fixed scanning positions and occlusion, using TLS to capture an entire tree is time-consuming. Additionally, the raw intensity of TLS data cannot accurately represent objects’ physical characteristics. Here, using LiDAR data acquired by an inhouse developed backpack Mobile Mapping System (MMS), we introduce a fast and fully unsupervised method that combines automatic thresholding of normalized radiometric and geometric features to extract major woody parts in the point clouds. We show that using MMS LiDAR data, our method can achieve higher performance than existing methods for major woody parts segmentation on 14 trees with different sizes and species in both leaf-on and leaf-off seasons.

Change detection is an important task in remote sensing (RS) image analysis. It is widely used in natural disaster monitoring and assessment, land resource planning, and other fields. As a pixel-to-pixel prediction task, change detection is sensitive about the utilization of the original position information. Recent change detection methods always focus on the extraction of deep change semantic feature, but ignore the importance of shallow-layer information containing high-resolution and fine-grained features, this often leads to the uncertainty of the pixels at the edge of the changed target and the determination miss of small targets. In this letter, we propose a densely connected siamese network for change detection, namely SNUNet-CD (the combination of Siamese network and NestedUNet). SNUNet-CD alleviates the loss of localization information in the deep layers of neural network through compact information transmission between encoder and decoder, and between decoder and decoder. In addition, Ensemble Channel Attention Module (ECAM) is proposed for deep supervision. Through ECAM, the most representative features of different semantic levels can be refined and used for the final classification. Experimental results show that our method improves greatly on many evaluation criteria and has a better tradeoff between accuracy and calculation amount than other state-of-the-art (SOTA) change detection methods.