Yue Wang

In the fields of geographic information science and location-based services, the fusion of multisource Point-of-Interest (POI) data is of remarkable importance but faces several challenges. Existing matching methods, including those based on single non-spatial attributes, single spatial geometric features, and traditional hybrid methods with fixed rules, suffer from limitations such as reliance on a single feature and inadequate consideration of spatial context. This study takes Dongcheng District, Beijing, as the research area and proposes a POI-matching method based on multi-feature value calculation and a deep neural network (DNN) model. The method comprehensively incorporates multidimensional features such as names, addresses, and spatial distances. Additionally, the approach also incorporates an improved multilevel name association strategy, an address similarity calculation using weighted edit distance, and a spatial distance model that accounts for spatial density and regional functional types. Furthermore, the method utilizes a deep learning model to automatically learn POI entity features and optimize the matching rules. Experimental results show that the precision, recall, and F1 value of the proposed method achieved 97.2%, 97.0%, and 0.971, respectively, notably outperforming traditional methods. Overall, this method provides an efficient and reliable solution for geospatial data integration and POI applications, and offers strong support for GIS optimization, smart city construction, and scientific urban/town planning. However, this method still has room for improvement in terms of data source quality and algorithm optimization.

The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching.

Identifying urban functional zones is an important task in urban planning and smart city construction. Accurately identifying urban functional zones and analyzing their spatial distribution is crucial for optimizing urban spatial layout, improving urban management and balancing human–environment interaction. However, most of the existing studies focus on the analysis of individual data sources, which have difficulty fully reflecting the complex spatial structures and functional differences in cities. To solve this problem, this paper proposes a new method of urban functional area identification which integrates multi-source data and advanced algorithms. By clustering the results of multi-factor weighted kernel density, the paper can more accurately quantify the spatial distribution characteristics of urban functional areas and better reflect the functional differences among urban areas. At the same time, this paper uses the Random Forest model to optimize the POI data and the building data to improve the classification accuracy and the generalization ability of the model. The results show that the distribution of functional areas in the Fifth Ring Road region of Beijing presents the characteristics of diversification and agglomeration: the core urban area is dominated by high-density commercial service and public service functional areas, with a high degree of functional integration; The peripheral areas are mainly residential areas and green areas, with dispersed distributions, but clear functions. The overall accuracy reaches 87%, and the model can effectively reflect the distribution and spatial characteristics of urban functional areas.

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters.

In urban point cloud scenarios, due to the diversity of different feature types, it becomes a primary challenge to effectively obtain point clouds of building categories from urban point clouds. Therefore, this paper proposes the Enhanced Local Feature Aggregation Semantic Segmentation Network (ELFA-RandLA-Net) based on RandLA-Net, which enables ELFA-RandLA-Net to perceive local details more efficiently by learning geometric and semantic features of urban feature point clouds to achieve end-to-end building category point cloud acquisition. Then, after extracting a single building using clustering, this paper utilizes the RANSAC algorithm to segment the single building point cloud into planes and automatically identifies the roof point cloud planes according to the point cloud cloth simulation filtering principle. Finally, to solve the problem of building roof reconstruction failure due to the lack of roof vertical plane data, we introduce the roof vertical plane inference method to ensure the accuracy of roof topology reconstruction. The experiments on semantic segmentation and building reconstruction of Dublin data show that the IoU value of semantic segmentation of buildings for the ELFA-RandLA-Net network is improved by 9.11% compared to RandLA-Net. Meanwhile, the proposed building reconstruction method outperforms the classical PolyFit method.