A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization
Bibliographic Data
| ID | 22034142 |
|---|---|
| Authors | Yi Xiao (0009-0005-4634-6677, Wuhan University), Tinghua Ai (0000-0002-6581-9872, Wuhan University, corresponding author), Min Yang (0000-0001-9017-5960, Wuhan University), Xiang Zhang (0000-0003-0965-7298, Wuhan University) |
| Year | 2020 |
| Volume | 9 |
| Issue | 4 |
| Pages | 239 |
| Publication date | 2020-04-11 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9040239 |
| OpenAlex | W3016038445 |
| Language | EN |
| Citations received | 2 |
| References cited | 33 |
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
3D city models · Cartographic generalization · Cartography · Data mining · Generalization · Geography · Personalization · Point of interest · Visualization · World Wide Web · 3D Modeling in Geospatial Applications · Automated Road and Building Extraction · Computer Science · Geographic Information Systems Studies · Mathematics · Artificial Intelligence
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |