Accurate acquisition of carbon sequestration information at the individual tree scale is crucial for the refined assessment and management of carbon stocks in urban ecosystems. Traditional optical remote sensing methods, limited by two-dimensional spectral features, are more suitable for large-scale forest carbon stock estimation but struggle to achieve refined quantification at the individual tree level. LiDAR technology, which directly characterizes three-dimensional structural parameters of trees through point clouds, significantly outperforms optical methods in improving both segmentation quantity and morphological accuracy. To achieve a refined assessment and management of the urban carbon storage, on the one hand, we propose to integrate UAV-borne and ground-based point clouds, thereby overcoming the observational limitations of single-source data. On the other hand, to address the problems of under- and over-segmentation and morphological distortion in individual tree segmentation. We have respectively proposed two extreme value segmentation models and a high-order energy morphological optimization model. Specifically, the Canopy Skyline Extremum (CSE) model can solve the under-segmentation problem caused by the absence of canopy extreme points and the over-segmentation problem caused by pseudo-extreme points from the perspective of the canopy dimension. The Vertical Distribution Extremum (VDE) model of tree can solve the under-segmentation problem caused by the sparsity of trunk point clouds from the trunk dimension. For crown morphology fidelity, we construct, a voxel-based spatial correlation graph is constructed to characterize the distribution of branches and leaves. We use the Markov Random Field (MRF) energy optimization framework, integrate the prior knowledge of tree ecology, establish a high-order energy model that satisfies the objective cognitive morphology of trees, dynamically assign the membership relationship of branches and leaves, effectively solve the problem of interlacing and adhesion of branches and leaves between adjacent trees, overcome the problem of tree morphological distortion caused by the "one-size-fits-all" approach in traditional individual tree segmentation, improve the calculation accuracy of the tree crown diameter and canopy volume, and enhance the estimation accuracy of the carbon storage of individual trees to the decimeter level. Experiments were conducted on six datasets from two typical urban scenarios: street trees and landscaped gardens. Results validate the superior performance of our morphology-faithful segmentation. Quantitative comparisons with the state-of-the-arts show that our approach achieves optimal performance in both segmentation accuracy and stability. Further analysis confirms that the proposed morphological optimization preserves reasonable tree shapes, leading to more accurate individual tree attribute calculations. This study verifies that the proposed models significantly enhance the accuracy of urban individual tree segmentation (quantity and morphology), enabling decimeter-level urban carbon stock assessment, and provides a novel technical framework for precise ecosystem carbon sequestration monitoring.

Large-size remote sensing images contain rich geographical information. Efficient and accurate semantic segmentation of these images is of significant importance in various fields. However, the massive memory requirements have hindered the development of semantic segmentation methods for large-size remote sensing images. Most existing methods struggle to balance memory usage, global modeling, and local representation accuracy. To address these issues, we propose a new semantic segmentation method for large-size remote sensing images, Mamba–CNN parallel network (MCPNet), which demonstrates impressive performance. The method is an asymmetric Mamba–convolutional neural network (CNN) hybrid architecture. Given the linear modeling complexity of Mamba, we construct the M-branch based on the visual state space (VSS) model, which processes downsampled images to reduce memory consumption while alleviating Mamba’s local forgetting problem. To further enhance the model’s capability in fine-grained detail extraction, we meticulously design a detail-preserving network (DPN) as the C-branch. This branch employs a split downsampling strategy and multiscale convolutional kernel groups to process large-size images, ensuring the preservation of spatial positional relationships while capturing fine-grained local details. Moreover, to effectively filter redundant information introduced by large-size images and bridge the semantic gap between the features extracted by CNN and Mamba, we propose a multigated feature fusion module (MG-FFM). This module progressively refines heterogeneous feature alignment through a bottom-up hierarchical refinement strategy, achieving a progressive fusion of semantics and details. Our method achieves state-of-the-art (SOTA) performance in terms of mean intersection over union (mIoU) and mF1 score on the self-constructed Yaan UAV dataset and two widely used public datasets (DeepGlobe and Inria Aerial) while consuming less GPU memory. The codes will be available athttps://github.com/fsqy-zhang/MCPNet

Three-dimensional (3D) line segments contain richer geometric and structural information than 3D point clouds in man-made environments, which is beneficial for providing constraints to refine point-cloud-based mesh models or build accurate wireframes. However, the efficient reconstruction of 3D line segments with high scene coverage from multi-view images is still challenging. In this study, the depth maps obtained from the point cloud generation procedure are exploited to decrease the search range of two-dimensional (2D) line segment correspondences to improve the efficiency, precision, and recall rate of 2D line segment matching, thereby improving the construction efficiency and scene coverage of 3D line segments. For a line segment on the reference image (called reference line segment) of an image pair, a reliable virtual line segment is produced by projecting several sampled points of the reference line segment onto the search image based on the corresponding depth information. Then, a purely geometrical similarity measurement under the constraints of the virtual line segment is designed to obtain 2D line segment matches. Using the 2D line segment correspondences of all image pairs, a multi-view clustering operation is performed to construct 3D line segments from the redundant 2D matches. Finally, a simple 3D-line-segment-based mesh model refinement method is designed and the reconstructed 3D line segments are employed to improve the quality of the point-cloud-based mesh model. In our experiments, five open-source datasets are adopted to qualitatively and quantitatively evaluate the performance of the proposed 3D line segment reconstruction method and the potential of 3D line segments on mesh model refinement. The experimental results show that the proposed 3D line segment reconstruction method performs better than the state-of-the-art methods. Specifically, on five open-source datasets, our method exhibits an average improvement of 47.28% in the number of reconstructed 3D line segments over the best one among the compared methods. Additionally, the experimental results of the mesh model refinement show that the addition of 3D line segments is beneficial for improving the quality of the point-cloud-based mesh model.

To register multiview, unordered point clouds from forest scenes, we must establish how scans are associated. The proposed method, called RegisMUF, can register arbitrary forest point clouds from aiming scenarios without knowledge of initial position and orientation, without requiring artificial targets, and without recording the order of the scanning sequents. One of the novel contributions of the proposed method is the optimization of a scanning network. We exploit common subgraphs to connect data between spatial subsets and subsequently predict the overlapping areas between adjacent scenarios. In parallel, we propose a rapid coarse strategy and an accurate tree-oriented refinement strategy. Finally, all the scenarios converge to an anchor by combining the minimum loop expansion approach and a parallel merging approach. We experimentally evaluate three challenging data sets: one data set from the FGI benchmark Evo, Finland with five scans, and two other forest data sets from Jiangxi province, China, with 15 and 23 scans. For each data set, the network-building module of RegisMUF produced 100% correct connections to associate all the scans. Together, 108 pairwise cases are exploited to evaluate the matching module in RegisMUF, and the results reveal that the proposed method is superior to or on par with state-of-the-art practices in terms of registration accuracy and successful-registration rate. In end-to-end tests, RegisMUF performs impressively both in terms of registration accuracy and computational cost.

This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid. To solve this problem, explicit exploration of the static region is necessary for both the coarse and fine registration steps. Fortunately, determining the corresponding regions can be eased by the intuitive realization that in urban environments, natural objects neither present straight boundaries nor stack vertically. This intuition has guided the authors to develop a robust approach for the detection of static regions using planar structures. Then, silhouettes of the objects are extracted from the planar structures, which assist in the determination of an SE(2) transformation in the horizontal direction by a novel line matching method. The silhouettes also enable identification of the correspondences of planes in the step of fine registration using a variant of the iterative closest point method. Experimental evaluations using point clouds of cargo ships with different sizes and shapes reveal the robustness and efficiency of the proposed method, which gives 100% success and reasonable accuracy in rapid time, suitable for an online system. In addition, the proposed method is evaluated systematically with regard to several practical situations caused by the floating platform, and it demonstrates good robustness to limited scanning time and noise.

A high-quality point field plays a crucial factor in scanner calibration. However, in most cases, efforts are made to find suitable algorithms and less attention is paid to establishing a point field. This letter focuses on the prediction and planning of quality point fields due to scanner calibration. We first propose specific criteria to assess whether the estimated calibration parameters are accurate enough for panoramic scanners. We relate a statistical bound for the unknown estimate deviations to the standard deviations of the scanner's raw measurements. We then apply the criteria and configuration requirements as the basic building blocks to design a point field and scanner setup plan to determine a subset of frequently used additional parameters (APs) to calibrate an indoor field with separately measured target coordinates. The provided strategy is the local optimal solution, as tested using Monte Carlo simulations and real calibration tasks.

This paper presents an image-guided end-to-end registration approach for globally consistent 3-D registration of unordered terrestrial laser scanning (TLS) point clouds. The proposed method can handle arbitrary point clouds with reasonable pairwise overlap without knowledge about their initial position and orientation, without requiring artificial targets, and without needing to record the order of the scanning. One of the novel contributions of the proposed approach lies in the optimization of a scanning network. We retrieve the similarities of all scans based on a vocabulary tree using both the geometrically rectified panorama images and the corresponding 3-D point clouds. The approach also highlights the integral optimization in both the coarse and fine registration. A pose graph is introduced to realize global optimization at the end of the coarse step without primitives. After that, the results act as the inputs to start the pairwise fine registration, which is then followed by the minimum loop expansion (MLE) refinement. Comprehensive experiments demonstrated network optimization rates of over 60% using the image-guided strategy. Using the pose-graph optimization method, successful registration rates (SRRs) increased to 100% for all tested cases. The MLE not only accelerates the speed of the convergence but also improves registration accuracy, which reached 0.1 m and 0.1° in the translation and rotation angles, respectively.