No abstract

Multi-scale object matching is the key technology for upgrading feature cascade and integrating multi-source spatial data. Considering the distinctiveness of data at different scales, the present study selects residential areas in a multi-scale database as research objects and focuses on characteristic similarities. This study adopts the method of merging with no simplification, clarifies all the matching pairs that lack one-to-one relationships and places them into one-to-one matching pairs, and conducts similarity measurements on five characteristics (i.e., position, area, shape, orientation, and surroundings). The relevance vector machine (RVM) algorithm is introduced, and the method of RVM-based spatial entity matching is designed, thus avoiding the needs of weighing feature similarity and selecting matching thresholds. Moreover, the study utilizes the active learning approach to select the most effective sample for classification, which reduces the manual work of labeling samples. By means of 1:5000 and 1:25,000 residential areas matching experiments, it is shown that the RVM method could achieve high matching precision, which can be used to accurately recognize 1:1, 1:m, and m:n matching relations, thus improving automation and the intelligence level of geographical spatial data management.

The identification of urban spatial functional units is of great significance in urban planning, construction, management, and services. Conventional field surveys are labour-intensive and time-consuming, while the abundant data available via the internet provide a new way to identify urban spatial functions. A major issue is in determining point of interest (POI) weights in urban functional zone identification using POI data. Along these lines, this work proposed a recognition method based on POI data combined with machine learning. First, the relationship between POI data and urban spatial function types was mapped, and the density of each type of POI was calculated. Then, the density values of each type of POI in the study unit were used as feature vectors and combined with the Kstar algorithm to identify urban spatial functions. Finally, the identification results were validated by combining multiple sources of POI data. From the acquired sampling results, it was demonstrated that the proposed method achieved an accuracy of 86.50%. The problem of human bias was also avoided in determining POI weights. High recognition accuracy was achieved, making urban spatial function recognition more accurate and automatable.

Entity matching is one of the key technologies for geospatial data update and fusion. In response to the shortcomings of most spatial entity matching methods that use local optimisation strategies, a global optimisation matching method for multi-representation buildings using road network constraints is proposed. First, the road network is used for region segmentation to obtain candidate matches. Second, the spatial similarity among the candidate matching objects is calculated and the characteristic similarity weights are determined using the entropy weight method. Third, the matching of building entities is transformed into an allocation problem using integer programming ideas, and the Hungarian algorithm is solved to obtain the optimal matching combination with minimum global variance. Finally, two test areas are selected to validate the proposed method, and the precision, recall, and F-measure values of the experiments are 96.35%, 97.11%, and 96.73% versus 95.96%, 97.03%, and 96.49%, respectively. The matching accuracy is greatly improved compared with the local search strategy.