The Use of Language Models to Support the Development of Cartographic Descriptions of a Building’s Interior

Abstract

The development and popularization of navigation applications are increasing expectations for their quality and functionality. Users need continuous navigation not only outdoors, but also indoors. In this case, however, the perception of space and movement is somewhat different than it is outside. One potential method of meeting this need may be the use of so-called geo-descriptions—multi-level textual descriptions relating to a point, line or area in a building. Currently, geo-descriptions are created manually. However, this is a rather time-consuming and complex process. Therefore, this study undertook to automate this process as much as possible. The study uses classical methods of spatial analysis from GIS systems and text generation methods based on artificial intelligence (AI) techniques, i.e., large language models (LLM). In this article, special attention will be paid to the second group of methods. As part of the first stage of the research, which was aimed at testing the proposed concept, the possibility of LLMs creating a natural description of space based on a list of features of a given place obtained by other methods (input parameters for AI), such as coordinates and categories of rooms around a given point, etc., was tested. The focus is on interior spaces and a few selected features of a particular place. In the next stages, it is planned to extend the research to spaces outside buildings. In addition, artificial intelligence can be used to provide the input parameters mentioned above.

Keywords

• indoor cartography
• GeoAI
• artificial intelligence
• large language models
• geoinformation
• indoor navigation