Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations

There are many 3D digital models of buildings with cultural heritage interest, but most of them lack semantic annotation that could be used to inform users of mobile and desktop applications about their origins and architectural features. We describe methods in an ongoing project for enriching 3D models with generic annotation, derived from examples of images of building components and from labelled plans and diagrams, and with object-specific descriptions obtained from photo captions. This is the first stage of research that aims to annotate 3D models with facts extracted from the text of authoritative architectural guides

Automatic semantic and geometric enrichment of CityGML building models using HoG-based template matching

Semantically rich 3D building models give the potential for a wealth of rich geo-spatially-enabled applications such as cultural heritage augmented reality, urban planning, radio network planning and personal navigation. However, the majority of existing building models lack much if any semantic detail. This work demonstrates a novel method for automatically locating subclasses of windows and doors, using computer vision techniques including the histogram of oriented gradient (HoG) template matching, and automatically creating enriched CityGML content for the matched windows and doors. Good results were achieved for class identification with potential for further refinement of subclasses of windows and doors and other architectural features. It is part of a wider project to bring even richer semantic content to 3D geo-spatial building models

Automatic semantic and geometric enrichment of CityGML 3D building models of varying architectural styles with HOG-based template matching

While the number of 3D geo-spatial digital models of buildings with cultural heritage interest is burgeoning, most lack semantic annotation that could be used to inform users of mobile and desktop applications about the architectural features and origins of the buildings. Additionally, while automated reconstruction of 3D building models is an active research area, the labelling of architectural features (objects) is comparatively less well researched, while distinguishing between different architectural styles is less well researched still. Meanwhile, the successful automatic identification of architectural objects, typified by a comparatively less symmetrical or less regular distribution of objects on façades, particularly on older buildings, has so far eluded researchers. This research has addressed these issues by automating the semantic and geometric enrichment of existing 3D building models by using Histogram of Oriented Gradients (HOG)-based template matching. The methods are applied to the texture maps of 3D building models of 20th century styles, of Georgian-Regency (1715-1830) style and of the Norman (1066 to late 12th century) style, where the amalgam of styles present on buildings of the latter style necessitates detection of styles of the Gothic tradition (late 12th century to present day). The most successful results were obtained when applying a set of heuristics including the use of real world dimensions, while a Support Vector Machine (SVM)-based machine learning approach was found effective in obviating the need for thresholds on matchscores when making detection decisions

Towards a National 3D Mapping Product for Great Britain

Knowing where something happens and where people are located can be critically important to understand issues ranging from climate change to road accidents, crime, schooling, transport and much more. To analyse these spatial problems, two-dimensional representations of the world, such as paper or digital maps, have traditionally been used. Geographic information systems (GIS) are the tools that enable capture, modelling, storage, retrieval, sharing, manipulation, analysis, and presentation of geographically referenced data. Three-dimensional geographic information (3D GI) is data that can represent real-world features as objects in 3D space. 3D GI offers additional functionality not possible in 2D, including analysing and querying volume, visibility, surface and sub-surface, and shadowing. This thesis contributes to the understanding of user requirements and other data related considerations in the production of 3D geographic information at a national level. The study promotes Ordnance Survey’s efforts in developing a 3D geographic product through: (1) identifying potential applications; (2) analysing existing 3D city modelling approaches; (3) eliciting and formalising user requirements; (4) developing metrics to describe the usefulness of 3D data and; (5) evaluating the commerciality of 3D GI. A review of current applications of 3D showed that visualisation dominated as the main use, allowing for better communication, and supporting decision-making processes. Reflecting this, an examination of existing 3D city models showed that, despite the varying modelling approaches, there was a general focus towards accurate and realistic geometric representation of the urban environment. Web-based questionnaires and semi-structured interviews revealed that while some applications (e.g. subsurface, photovoltaics, air and noise quality) lead the field with a high adoption of 3D, others were laggards due to organisational inertia (e.g. insurance, facilities management). Individuals expressed positive views on the use of 3D, but still struggled to justify the value and business case. Simple building geometry coupled with non-building thematic classes was perceived to be most useful by users. Several metrics were developed to quantify and compare the characteristics of thirty-three 3D datasets. Results showed that geometry-based metrics such as minimum feature length or Euler characteristic can be used to provide additional information as part of fitness-for-purpose evaluations. The metrics can also contribute to quality control during data production. An investigation into the commercial opportunities explored the economic value of 3D, the market size of 3D data in Great Britain, as well as proposed a number of opportunities within the wider business context of Ordnance Survey

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available