Individual tree skeletonization is a fundamental task in forestry remote sensing, which serves as a crucial prerequisite for various downstream applications, ranging from tree structural attribute estimation to carbon cycle modeling. Nevertheless, most existing skeletonization approaches struggle to generate a compact, centered tree skeleton while preserving detail fidelity and topological rationality. To this end, this paper proposes a water droplet model-driven entropy optimization approach (WDTS) for individual tree skeletonization from Terrestrial Laser Scanning (TLS) point clouds. WDTS models an individual tree TLS point cloud as a system of water droplets with varying masses, by progressively generating the skeleton through simulated droplet contraction, merging, and evaporation processes. Key to our approach is an entropy reduction framework that progressively drives droplets toward compact skeletons. To further enhance the centeredness of the generated tree skeleton, WDTS employs a geometric and topological interwoven optimization strategy, explicitly aligning the skeleton within the center of the branch point clouds by minimizing the sum of the squared residuals. Experiments conducted on three individual tree TLS point cloud datasets with different data acquisition strategies have demonstrated the effectiveness and robustness of the proposed WDTS. Compared with previous methods, especially the state-of-the-art Dijkstra-enhanced L1-medial method, WDTS remarkably improves the compactness and centeredness of the skeletons with well-preserved local branch details, reducing the averaged (Formula presented) by (Formula presented), (Formula presented), and (Formula presented) on the single-scan, multi-scan, and simulated dataset, respectively. The generated tree skeletons, including not only the tree skeleton points but also topologically coherent edges, provide a robust foundation for downstream tasks, including precise tree geometry modeling, biomass estimation, and forestry-related sustainable development applications. The code of the proposed WDTS is available at https://github.com/Putaonjfu/WDTS.

Highlights: What are the main findings? We propose a dual-constraint edge-collapse simplification method, jointly enforcing structural constraints and semantic constraints to generate lightweight 3D building models while maintaining high geometric accuracy and semantic consistency. Experiments on Sketchfab, ArCH, STPLS3D and SUM datasets demonstrate the superiority of the proposed method in preserving key structural features of building models under high compression ratios compared to existing methods. What are the implications of the main findings? We provide an effective approach for building model simplification, which is particularly useful for mitigating fine-scale structural degradation and semantic discontinuities commonly observed in conventional methods. The generated lightweight building models are enriched with both geometric details and semantic context, supporting CityGML-compliant 3D model storage, intelligent management, and downstream analysis in digital twin applications. Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.

The concept of historic gardens has gradually expanded to encompass a broader range of landscape meanings. UNESCO's cultural landscape categories have significantly influenced land policy improvements in the context of globalization, with historic gardens being classified as Category 1 cultural landscapes. The other categories are organically evolved landscapes (Category 2) and associative cultural landscapes (Category 3). While existing studies have primarily focused on each of these categories individually, it remains unclear how to characterize a cultural landscape when all three categories coexist and influence each other, as seen in complex cases such as the Chengde Mountain Resort (CMR). Furthermore, strategies for improving sustainable land management based on this understanding are still lacking. This study uses landscape mapping to collect data, digitally reconstruct, and characterize cultural landscapes in the CMR based on four environmental factors: topography, accessibility, visibility, and land use changes. Based on this, we illustrate the evolution of the CMR through reconstruction, capturing four phases detailed in 144 scenes. From this, we identify six distinct groups of scenes with six targeted indicators, each reflecting specific spatial attributes of Category 1. Additionally, statistical and comparative analyses of land use changes illuminate various landscape dynamics of these scenes that correspond to Categories 2 and 3. The discussion presents a systematic sustainable pathway to characterize the interdependencies among UNESCO’s three cultural landscape categories. Based on these findings, this research proposes a three-level management model that connects dynamic authenticity and modern functionality, offering insights for urban policymakers navigating pluralistic cultural landscapes.

Although there have been numerous studies on the heritage attributes, characteristics, and values of the historic garden as a special category of cultural heritage, the question is why a comprehensive review combining mainstream historic garden conservation with ways of understanding the garden in a landscape context has not been conducted. Landscape is an integrative concept that combines physical features and the diversity of functions with social and ecological processes throughout the scales of time and space. Therefore, this landscape context means applying the landscape approach to explore the organic connection between the scale of evolution and the architectonic elements in relation to each other. To elaborate, instead of viewing the garden as an object in one specific temporal-spatial frame, such an approach focuses on the evolution of the site in order to identify persistent structures and other values. The method used in this study involved paper coding as qualitative analysis combined with bibliometric visualization software. We reviewed 162 studies to explore the interconnections between the historic garden and landscape approach. The result is that there are three correspondences between landscape approaches and different stages of the historic garden’s conservation and development: studies identifying the historic garden’s characteristics using landscape mapping, studies demonstrating historic gardens’ conservation based on landscape planning, and studies exploring the potential of development and reuse through landscape design. Finally, we discuss the research gaps and outline an action framework for the conservation and development of heritage gardens in a landscape context.