Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e. g., OpenStreetMap and existing (Geo)KGs (e. g., Wikidata, DBPedia, and GeoNames), and to perform queries combining information from multiple data sources.
In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1.
A vast amount of geographic information exists in natural language texts, such as tweets and news.
Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing.
Navigation services utilized by autonomous vehicles or ordinary users require the availability of detailed information about road-related objects and their geolocations, especially at road intersections.