The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results.

Identifying urban production–living–ecological spaces and their interactive relationships is conducive to better understanding and optimizing urban space development. This paper took the main urban area of Hangzhou city as an example, and a two-level scoring evaluation model was constructed to accurately identify urban production–living–ecological spaces using point of interest (POI) data. Then, kernel density analysis, a spatial transfer matrix, and a bivariate spatial autocorrelation model were used to reveal the spatial patterns of urban production–living–ecological spaces and their interactive relationships during 2010 and 2019. The results showed that the proposed two-level scoring evaluation model combining both the physical area and density of POIs was effective in accurately identifying urban production–living–ecological spaces using POI data, with an identification accuracy of 88.9%. Urban production space was concentrated on the south bank of the Qiantang River and around the north of Hangzhou. Urban living space had the highest proportion, mainly distributed within the ring highway of Hangzhou in a contiguous distribution pattern, and urban ecological space was concentrated around West Lake and Xiang Lake. During 2010 and 2019, the expansion of urban production–living–ecological spaces had obvious spatial differences. Additionally, the mutual transformation between production and living spaces was more frequent during the study period and was mainly distributed within the ring highway of Hangzhou. There were significant positive spatial correlations between production and living and between living and ecological spaces, while a significant negative spatial correlation occurred between production and ecological spaces. The spatial correlations of urban production–living–ecological spaces revealed obvious spatial heterogeneity. This study proposed a two-level scoring evaluation model to accurately identify the spatial patterns of urban production–living–ecological spaces and their interactive relationships using POI data, which can provide detailed information and scientific references for urban spatial planning and management in rapidly urbanizing cities.

Orbital angular momentum (OAM) multiplexing technology has great potential in high capacity optical communication. OAM superimposed mode can extend communication channels and thus enhance the capacity, and accurate recognition of multi-OAM superimposed mode at the receiver is very crucial. However, traditional methods are inefficient and complex for the recognition task. Machine learning and deep learning can offer fast, accurate and adaptable recognition, but they also face challenges. At present, the OAM mode recognition mainly focus on single OAM mode and ±l superimposed dual-OAM mode, while few researches on multi-OAM superimposed mode, due to the limitations of single-object image classification techniques and the diversity of features to recognize. To this end, we develop a recognition method combined with multi-label image classification to accurately recognize multi-OAM superimposed mode vortex beams. Firstly, we create datasets of intensity distribution map of three-OAM and four-OAM superimposed mode vortex beams based on numerical simulations and experimental acqusitions. Then we design a progressive channel-spatial attention (PCSA) model, which incorporates a progressive training strategy and two weighted attention modules. For the numerical simulation datasets, our model achieves the highest average recognition accuracy of 94.9% and 91.2% for three-OAM and four-OAM superimposed mode vortex beams with different transmission distances and noise strengths respectively. The highest experimental average recognition accuracy for three-OAM superimposed mode achieves 92.7%, which agrees with the numerical result very well. Furthermore, our model significantly outperforms in most metrics compared with ConvNeXt, and all experiments are within the affordable range of computational cost.

Structure from Motion (SfM) for large-scale UAV (Unmanned Aerial Vehicle) images has been widely used in the fields of photogrammetry and computer vision. Its efficiency, however, decreases dramatically as well as with the memory occupation rising steeply due to the explosion of data volume and the iterative BA (bundle adjustment) optimization. In this paper, an efficient SfM solution is proposed to solve the low-efficiency and high memory consumption problems. First, an algorithm is designed to find UAV image match pairs based on a graph-indexed bag-of-words (BoW) model (GIBoW), which treats visual words as vertices and link relations as edges to build a small-world graph structure. The small-world graph structure can be used to search the nearest-neighbor visual word for query features with extremely high efficiency. Reliable UAV image match pairs can effectively improve feature matching efficiency. Second, a central bundle adjustment with object point-wise parallel construction of the Schur complement (PSCBA) is proposed, which is designed as the combination of the LM (Levenberg–Marquardt) algorithm with the preconditioned conjugate gradients (PCG). The PSCBA method can dramatically reduce the memory consumption in both error and normal equations, as well as improve efficiency. Finally, by using four UAV datasets, the effectiveness of the proposed SfM solution is verified through comprehensive analysis and comparison. The experimental results show that compared with Colmap-Bow and Dbow2, the proposed graph index BoW retrieval algorithm improves the efficiency of image match pair selection with an acceleration ratio ranging from 3 to 7. Meanwhile, the parallel-constructed BA optimization algorithm can achieve accurate bundle adjustment results with an acceleration ratio by 2 to 7 times and reduce memory occupation by 2 to 3 times compared with the BA optimization using Ceres solver. For large-scale UAV images, the proposed method is an effective and reliable solution.

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

Although T-form buildings have been widely observed in newly constructed high-rise residential communities, there have been relatively limited investigations into the influence of their geometries on wind environments. This study aims to address this gap by conducting iterative quantitative assessments of the influences of various bulge-part sizes of typical T-form high-rise residential buildings on surrounding wind environments. A methodology has been employed by integrating multiple computational tools, including parametric modeling, Computational Fluid Dynamics (CFD), and Influenced Areas (IAs) analysis. Representative T-form buildings have been modeled with parametric components, allowing for easy variation of bulge-part sizes. The investigation process involves sequential steps of parametric modeling, experimentally validated CFD simulations, statistical assessment, and subsequent results analysis and discussions. Findings could be summarized as follows: (1) according to IAs analysis, the influences on wind environments were decreased as the bulge-part sizes were increased, and the decrease of the bulge-part sizes could cause the contrary effect; (2) the promotion of outdoor ventilation caused by the increase of the bulge-part length was more than the increase of the bulge-part width according to the correlation coefficients (0.88 > 0.78; 0.88 > 0.76); (3) it was recommended to design relatively large bulge parts on the windward side to enhance outdoor ventilation. The research outcomes provide valuable and insightful information for the development of sustainable architectural design strategies aimed at optimizing natural ventilation.