Over the past several years, volunteered geographic information (VGI) has expanded rapidly. VGI collection has been proven to serve as a highly successful means of acquiring timely and detailed global spatial data. However, VGI includes several special properties. For example, the contributor’s reputation affects the quality of objects edited, and a geographic object may have multiple versions. The existing spatio-temporal data model cannot describe the unique properties of VGI. Therefore, a spatio-temporal VGI model considering trust-related information is presented in this paper. In this model, central elements of the VGI environment, e.g., geographic entity, entity state, state version, contributor, reputation, geographic event, and edit event, and their interaction mechanisms are analysed. Major VGI objects and relations are determined using the object-oriented method and trust-related operations, and their relationships are analysed, and nine linkage rules among trust-related operations are found to maintain the consistency of corresponding data. A prototype system for the spatio-temporal VGI model is presented, and the effectiveness of the model is verified.

With the continuous development of indoor positioning technology, various indoor applications, such as indoor navigation and emergency rescue, have gradually received widespread attention. Indoor navigation and emergency rescue require access to a variety of indoor space information, such as accurate geometric information, rich semantic information and indoor spatial adjacency information; hence, a suitable 3D indoor model is needed. However, the available models, such as BIM and CityGML, mainly represent geometric and semantic information of indoor spaces, and rarely describe the topological adjacency relationship of interior spaces. To address the requirements of indoor navigation and emergency rescue, a simplified 3D indoor model is proposed in this research. The building components and indoor functional spaces of buildings are described in a simplified way. The geometric and semantic information are described based on CityGML, and the topological relationships of indoor adjacent spaces are represented by CityGML XLinks. While describing the indoor level of detail (LOD) of buildings in detail, the model simplifies building components and indoor spaces, which can preserve the characteristics of indoor spaces to the maximum extent and serve as a basis for indoor applications.

Multi-temporal remote sensing images are the primary sources for change detection. However, it is difficult to obtain comparable multi-temporal images at the same season and time of day with the same sensor. Considering texture homogeneity among objects belonging to the same category, this paper presents a new change detection approach using a texture feature space outlier index from mono-temporal remote sensing images and vector data. In the proposed approach, a texture feature contribution index (TFCI) is defined based on information gain to select the optimal texture features, and a feature space outlier index (FSOI) based on local reachability density is presented to automatically identify outlier samples and changed objects. Our approach includes three steps: (1) the sampling method is designed considering spatial distribution and topographic properties of image objects extracted by segmenting the recent image with existing vector map. (2) Samples with changed categories are refined by an iteration procedure of texture feature selection and outlier sample elimination; and (3) the changed image objects are identified and classified using the refined samples to calculate the FSOI values of the image objects. Three experiments in the two study areas were conducted to validate its performance. Overall accuracies of 95.94%, 96.36%, and 96.28% were achieved, respectively, while the omission and commission errors for every category were all very low. Four widely used methods with two-temporal images were selected for comparison, and the accuracy of the proposed method is higher than theirs. This indicates that our approach is effective and feasible.

Spatial data are fundamental for borderland analyses of geography, natural resources, demography, politics, economy, and culture. As the spatial data used in borderland research usually cover the borderland regions of several neighboring countries, it is difficult for anyone research institution of government to collect them. Volunteered Geographic Information (VGI) is a highly successful method for acquiring timely and detailed global spatial data at a very low cost. Therefore, VGI is a reasonable source of borderland spatial data. OpenStreetMap (OSM) is known as the most successful VGI resource. However, OSM's data model is far different from the traditional geographic information model. Thus, the OSM data must be converted in the scientist’s customized data model. Because the real world changes rapidly, the converted data must be updated incrementally. Therefore, this paper presents a method used to dynamically integrate OSM data into the borderland database. In this method, a basic transformation rule base is formed by comparing the OSM Map Feature description document and the destination model definitions. Using the basic rules, the main features can be automatically converted to the destination model. A human-computer interaction model transformation and a rule/automatic-remember mechanism are developed to interactively transfer the unusual features that cannot be transferred by the basic rules to the target model and to remember the reusable rules automatically. To keep the borderland database current, the global OsmChange daily diff file is used to extract the change-only information for the research region. To extract the changed objects in the region under study, the relationship between the changed object and the research region is analyzed considering the evolution of the involved objects. In addition, five rules are determined to select the objects and integrate the changed objects with multi-versions over time. The objects’ change-type evolution is analyzed, and seven rules are used to determine the change-type of the changed objects. Based on these rules and algorithms, we programmed an automatic (or semi-automatic) integrating and updating prototype system for the borderland database. The developed system was intensively tested using OSM data for Vietnam and Pakistan as the experimental data.

As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development.

Borderlands modeling and understanding depend on both spatial and non-spatial data, which were difficult to obtain in the past. This has limited the progress of borderland-related research. In recent years, data collection technologies have developed greatly, especially geospatial Web 2.0 technologies including blogs, publish/subscribe, mashups, and GeoRSS, which provide opportunities for data acquisition in borderland areas. This paper introduces the design and development of a Geoweb-based tagging system that enables users to tag and edit geographical information. We first establish the GeoBlog model, which consists of a set of geospatial components, posts, indicators, and comments, as the foundation of the tagging system. GeoBlog is implemented such that blogs are mashed up with OpenStreetMap. Moreover, we present an improvement to existing publish/subscribe systems with support for spatio-temporal events and subscriptions, called Spatial Publish/Subscribe, as well as the event agency network for routing messages from the publishers to the subscribers. A prototype system based on this approach is implemented in experiments. The results of this study provide an approach for asynchronous interaction and message-ordered transfer in the tagging system.