Emerging topics in 3D GIS

This section of Transactions in GIS “Emerging topics in 3D GIS” represents a collection of approaches to acquire, analyse, and utilise 3D geospatial and Building Information Modelling (BIM) data. The issue aims at providing an insight in the latest developments and applications in advanced 3D data and technologies, encompassing topics from 3D city models acquisition and processing to BIM and 3D spatial data analysis. A part of this special issue arises from the 3D GeoInfo 2019 Conference and the 2th BIM/GIS Integration Workshop organised in Singapore (Stouffs et al., 2019a,b). The authors were given an opportunity to extend their research papers published in the proceedings of the event, but this issue was also open to other researchers working on the state of the art of the topics covered by the event. This special issue consists of eleven papers that represent the latest emerging topics in 3D GIS and cover a number of important aspects of 3D spatial data science and BIM. This issue opens with the paper “Area and volume computation of longitude–latitude grids and three-dimensional meshes” by Kelly and Šavrič (2021). The paper presents relevant geodetic considerations for longitude-latitude grid and three-dimensional mesh analyses and demonstrates the effects of neglecting these aspects on global and regional data analyses using GIS. Using ecological marine units (EMUs), the paper compiles and compares calculations of grid cell surface area and mesh element volume on spherical and ellipsoid Earth models, confirming that ellipsoidal equations yield the best results. The second paper “Web-based real-time visualization of large-scale weather radar data using 3D tiles”, by Lu et al. (2021), argues that existing weather studies are mostly confined to 2D and proposes a web-based real-time 3D visualization framework for large-scale weather radar data using 3D Tiles. The researchers develop a prototype Meteo3D presenting a nation-wide implementation of the framework. Dehbi et al. (2021b) concentrate on advancing the automatic acquisition of 3D building models, by focusing on improving the reconstruction of dormers. The paper presents a novel approach that classifies and reconstructs roofs and their structures based on designed statistical features derived from probability density functions. The presented approach identifies even rather small dormers, and it contributes to the increase of detail of 3D building models. In the work focused on indoor of buildings, Hu et al. (2021) underline the importance of semantic information of indoor features. The researchers develop an approach to infer room usage information, and use two techniques: random forest and relational graph convolutional networks. The experiments are conducted on floor plans of university research buildings. Dehbi et al. (2021a) propose a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data using an active sampling strategy. Following machine learning approaches and using prior knowledge represented by density distributions, the method overcomes the pitfalls of RANSAC-based approaches and validates promising hypotheses. Eriksson et al. (2021) assert the importance of version management in 3D geoinformation, as one of the prerequisites for a digital information flow in the planning