As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.

Geospatial knowledge is the result of spatial thinking and reasoning about natural and human phenomena (Golledge 2002), playing an important role in urban planning, land management, and environmental protection. Digital vector maps are important sources of geospatial knowledge such as topographic maps, land cover maps, traffic maps, city maps, etc. Maps, distinguished by spatial cognition expression symbols, are regarded as the second language of geoscience research. Maps are not only a means for humans to explain geographical phenomena, but they are also a significant instrument for gaining spatial information. Humans get spatial knowledge about geographic objects' location, shape, size, spatial pattern, etc. from maps via intuitive visual perception, computational geometry, spatial statistics, and other spatial operation methods. Driven by data collection technologies, artificial intelligence, and network technology, map audiences are shifting from human-oriented services to intelligent machine-oriented services. Map service has encountered new challenges in intelligent machine application fields (e.g., spatial scene perception for autonomous driving, semantic navigation of indoor robots, etc.)-heterogeneous spatial semantics, insufficient representation of spatial knowledge, and inadequate knowledge reasoning ability (Zhang et al. 2022). Traditional spatial representation with geometric symbols benefits human visual perception, geometric operations, and statistical analysis, but it is unsuitable for machine-oriented services. To expand geospatial knowledge services and promote the development of knowledge-driven research, a geographic knowledge representation model that can be interpreted and calculated by machines is required.

Remotely sensed imagery and field surveying have substantially contributed to the universal availability of land-use maps. Presently, multi-scale cartography techniques have also facilitated visual representations of land use. However, the multitemporal, multi-scale, and multi-category characteristics of these data often lead to diversity and complexity in crossscale outcomes (Zhu et al., 2022). The main factors are due to: (1) the single format of traditional fundamental geomatics products and the lag in updating at the map sectors; (2) the fragmented abstract representation and modeling at separate scales; and (3) the complicated intertwined geometric patterns, topological correlations and semantic attributes of landuse data at different scales. These cartographic mission discrepancies and incomplete perception regarding target scales may contribute to misjudgments in macro decision-making, overall planning, and mapping exports on land information. In Figure 1, for example, the results are misleading land information visualizations at different scales in some inappropriate ways. The centerline extraction at the end of the braided bayous is not satisfactory when smoothing the lake boundaries. New redundant regions may be triggered at adjacent land use necks during scaling.