The accurate leaf-wood separation of individual trees from point clouds is an important yet challenging task. Many existing methods rely on manual features that are time-consuming and labor-intensive to distinguish between leaf and wood points. However, due to the complex interlocking structure of leaves and wood in the canopy, these methods have not yielded satisfactory results. Therefore, this paper proposes an end-to-end LWSNet to separate leaf and wood points within the canopy. First, we consider the linear and scattering distribution characteristics of leaf and wood points and calculate local geometric features with distinguishing properties to enrich the original point cloud information. Then, we fuse the local contextual information for feature enhancement and select more representative features through a rearrangement attention mechanism. Finally, we use a residual connection during the decoding stage to improve the robustness of the model and achieve efficient leaf-wood separation. The proposed LWSNet is tested on eight species of trees with different characteristics and sizes. The average F1 score for leaf-wood separation is as high as 97.29%. The results show that this method outperforms the state-of-the-art leaf-wood separation methods in previous studies, and can accurately and robustly separate leaves and wood in trees of different species, sizes, and structures. This study extends the leaf-wood separation of tree point clouds in an end-to-end manner and demonstrates that the deep-learning segmentation algorithm has a great potential for processing tree and plant point clouds with complex morphological traits.

Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree.

The urban heat island (UHI) effect in cities and its driving factors have long been investigated. 3-D buildings are key components of urban structures and have notable effect on the UHI effect. However, due to the incomplete 3-D building information in urban database, only a few studies investigated the impact of 3-D building morphology factors on the land surface temperature (LST). In this study, a total set of 14 2-D and 3-D building morphology factors were selected to investigate the correlation between UHI and building morphology across three megacities in China (Beijing, Wuhan, Shanghai) with Landsat-8 LST scenarios in summer and winter of 2018 and 2019, respectively. Both Spearman correlation and partial correlation analysis were applied at block scale after urban functional zone (UFZ) mapping. A number of significant observations were noted based on the multispatial and multitemporal experimental results:1) both 2-D and 3-D building morphology factors influence LST, among them building coverage ratio, building ground area divided by facade area (GA2FA), and sky view factor yield stronger correlations with LST; 2) UFZ mapping scheme and partial correlation method help with controlling the anthropogenic heat release, greenspace, and water bodies coverage variables when targeting at the influence of building morphology factors on LST. This study provides insights for building morphology design in future urban planning and management.

The representation quantifies the geometric shape and topology of a building is a necessary procedure for many urban planning applications. A sharp line framework is a high-level structural cue providing a compact building representation. However, accurate and efficient structural line extraction remains a challenging task given the variety and complexity of buildings. This study proposes a general 3-D structural line extraction method from point clouds. The building points are extracted and further divided into various single-building units. In the proposed 3-D structural line extraction method, individual building point cloud is the input. First, the corners are detected by an associative learning module. Next, the curve connection is implemented by a link prediction block based on the graph neural network (GNN) embedded with corner information. After that, the obtained curves are subsequently converted into a topological graph. Finally, the corner points are optimized to achieve precise fitting of the structural lines. The experiments and comparisons on two airborne laser scanning (ALS) point cloud datasets demonstrate the effectiveness of the proposed method and the ability to retrieve ideal structural line results for building point clouds. Furthermore, without reprocessing, the proposed method yielded better results for various dataset types (outdoor building, indoor scene, and furniture point clouds) than the prevalent published methods ( i.e ., EC-Net, PIE-Net, and PC2WF), verifying its strength and efficacy. To further verify the accuracy of the obtained structural lines, we also introduce a line-based model reconstruction method that employ these lines for building reconstruction.

Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in geoinformation production. As a critical step for interpretation, accurate semantic segmentation can considerably broaden various applications of ALS data. However, most existing methods cannot provide precise annotations and high robustness due to occlusions, varied point densities, and complex and incomplete object structures. Therefore, we developed a semantic segmentation framework focusing on ALS point clouds. The framework comprises contextual feature extraction from a local neighborhood, scene-aware global information representation, and a ground-aware attention module. To verify its effectiveness, comprehensive experiments were conducted on three airborne light detection and ranging (LiDAR) datasets: DublinCity, Dayton Annotated LiDAR Earth Scan (DALES), and DFC2019 datasets. The experimental results demonstrate that the proposed method achieves better segmentation performance than that some advanced methods. For the DublinCity dataset, our model’s overall accuracy (OA) can be improved to 67.5% with an average F ₁ (Avg $F_{1}$ ) of 37.6%. For the DALES dataset, our method achieved an OA of 96.5% and a mean intersection over union (mIoU) of 77.6%. Our method also achieves a more accurate result on the DFC2019 dataset than that obtained using other models with an OA of 94.8% and an AvgF ₁ of 81.4%.