Reconstructing historical 3D city models

Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to two dimensions. We investigate in this paper how historical maps can be ‘augmented’ with the third dimension so that buildings have heights, volumes, and roof shapes. The resulting 3D city models, also known as digital twins, have several benefits in practice since it is known that some spatial analyses are only possible in 3D: visibility studies, wind flow analyses, population estimation, etc. At this moment, reconstructing historical models is (mostly) a manual and very time-consuming operation, and it is plagued by inaccuracies in the 2D maps. In this paper, we present a new methodology to reconstruct 3D buildings from historical maps, we developed it with the aim of automating the process as much as possible, and we discuss the engineering decisions we made when implementing it. Our methodology uses extra datasets for height extraction, reuses the 3D models of buildings that still exist, and infers other buildings with procedural modelling. We have implemented and tested our methodology with real-world historical maps of European cities for different times between 1700 and 2000

The effect of food environments on fruit and vegetable intake as modified by time spent at home: a cross-sectional study.

OBJECTIVE: There is a growing body of research that investigates how the residential neighbourhood context relates to individual diet. However, previous studies ignore participants' time spent in the residential environment and this may be a problem because time-use studies show that adults' time-use pattern can significantly vary. To better understand the role of exposure duration, we designed a study to examine 'time spent at home' as a moderator to the residential food environment-diet association. DESIGN: Cross-sectional observational study. SETTINGS: City of Toronto, Ontario, Canada. PARTICIPANTS: 2411 adults aged 25-65. PRIMARY OUTCOME MEASURE: Frequency of vegetable and fruit intake (VFI) per day. RESULTS: To examine how time spent at home may moderate the relationship between residential food environment and VFI, the full sample was split into three equal subgroups--short, medium and long duration spent at home. We detected significant associations between density of food stores in the residential food environment and VFI for subgroups that spend medium and long durations at home (ie, spending a mean of 8.0 and 12.3 h at home, respectively--not including sleep time), but no associations exist for people who spend the lowest amount of time at home (mean=4.7 h). Also, no associations were detected in analyses using the full sample. CONCLUSIONS: Our study is the first to demonstrate that time spent at home may be an important variable to identify hidden population patterns regarding VFI. Time spent at home can impact the association between the residential food environment and individual VFI

Harmonising the OGC Standards for the Built Environment: A CityGML Extension for LandInfra

The relatively new Open Geospatial Consortium (OGC) standard LandInfra documents in its data model land and civil engineering infrastructure features. It has a Geography Markup Language (GML) implementation, OGC InfraGML, which has essentially no software support and is rarely used in practice. In order to share the benefits of LandInfra (and InfraGML) with a wider public, we have created the Infra Application Domain Extension (ADE), a CityGML ADE that allows us to store LandInfra features in CityGML. In this paper, we semantically map LandInfra to CityGML, describe our ADE, and discuss a few used cases where our ADE can be useful for applications for the built environment. We also provide software to automatically convert datasets from InfraGML to CityGML (and our ADE), and vice versa, as well as to validate them, which will help practitioners generate real-world InfraGML datasets

3D building metrics for urban morphology

10.1080/13658816.2022.2103818International Journal of Geographical Information Science1-3