Automatic classification of building types in 3D city models

Henn, André; Römer, Christoph; Gröger, Gerhard; Plümer, Lutz

doi:10.1007/s10707-011-0131-x

Cited by 55 publications

(33 citation statements)

References 25 publications

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“…LOD1 models provide a relatively high information content and usability comparing to their geometric detail (Henn et al, 2012;Hofierka and Zlocha, 2012). For instance, they may be used for shadowing simulations (Strzalka et al, 2012;Alam et al, 2013;Li et al, 2015), estimation of noise pollution (Stoter et al, 2008), energy demand estimation (Strzalka et al, 2011;Bahu et al, 2013), simulating floods (Varduhn et al, 2015), analysing wind comfort (Amorim et al, 2012), and visualisation (Gesquière and Manin, 2012).…”

Section: Lod0 and Lod1 Familiesmentioning

confidence: 99%

An improved LOD specification for 3D building models

Biljecki

Ledoux

Stoter

2016

Computers, Environment and Urban Systems

331

176

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The level of detail (LOD) concept of the OGC standard CityGML 2.0 is intended to differentiate multi-scale representations of semantic 3D city models. The concept is in practice principally used to indicate the geometric detail of a model, primarily of buildings. Despite the popularity and the general acceptance of this categorisation, we argue in this paper that from a geometric point of view the five LODs are insufficient and that their specification is ambiguous.We solve these shortcomings with a better definition of LODs and their refinement. Hereby we present a refined set of 16 LODs focused on the grade of the exterior geometry of buildings, which provide a stricter specification and allow less modelling freedom. This series is a result of an exhaustive research into currently available 3D city models, production workflows, and capabilities of acquisition techniques. Our specification also includes two hybrid models that reflect common acquisition practices. The new LODs are in line with the LODs of CityGML 2.0, and are intended to supplement, rather than replace the geometric part of the current specification. While in our paper we focus on the geometric aspect of the models, our specification is compatible with different levels of semantic granularity. Furthermore, the improved LODs can be considered format-agnostic.Among other benefits, the refined specification could be useful for companies for a better definition of their product portfolios, and for researchers to specify data requirements when presenting use cases of 3D city models. We support our refined LODs with experiments, proving their uniqueness by showing that each yields a different result in a 3D spatial operation.Keywords: Level of detail; 3D city modelling; 3D GIS; 3D building models; CityGML; Scale Highlights• CityGML LODs are an industry standard for conveying the grade of 3D city models.• The 5 LODs are not defined precisely, and they are not sufficient for this purpose.• We present a refined series of 16 LODs that overcomes these issues.

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Section: Lod0 and Lod1 Familiesmentioning

confidence: 99%

An improved LOD specification for 3D building models

Biljecki

Ledoux

Stoter

2016

Computers, Environment and Urban Systems

331

176

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“…Henn et al (2012) use supervised learning (support vector machines -SVM) automatically classifying the type of building from LOD1 models containing a few building attributes (e.g. number of storeys) and several attributes about the surrounding context (e.g.…”

Section: Related Workmentioning

confidence: 99%

Estimating Building Age With 3d Gis

Biljecki

Sindram

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

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“…This knowledge-based approach provides already rough national figures on the structure of the building stock, but further model enhancements might be feasible for a more detailed differentiation of the buildings (e.g., apartment building, single family homes). In this context, a more comprehensive set of attributes, which takes proximity of buildings into account, has to be calculated and evaluated, and data-driven approaches for building classification may be considered that make use of modern pattern recognition and machine learning techniques (e.g., [34,38,46,65]). In addition, new data products will be available in future containing information of building height and roof type with full coverage (e.g., 3D building model of Germany).…”

Section: Limits To Quantification Of Building Structurementioning

confidence: 99%

A Workflow for Automatic Quantification of Structure and Dynamic of the German Building Stock Using Official Spatial Data

Hartmann

Meinel

Hecht

et al. 2016

IJGI

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Knowledge of the German building stock is largely based on census data and annual construction statistics. Despite the wide range of statistical data, they are constrained in terms of temporal, thematic and spatial resolution, and hence do not satisfy all requirements of spatial planning and research. In this paper, we describe a new workflow for data integration that allows the quantification of the structure and dynamic of national building stocks by analyzing authoritative geodata. The proposed workflow has been developed, tested and demonstrated exemplarily for the whole country of Germany. We use nationwide and commonly available authoritative geodata products such as building footprint and address data derived from the real estate cadaster and land use information from the digital landscape model. The processing steps are (1) data preprocessing; (2) the calculation of building attributes; (3) semantic enrichment of the building using a classification tree; (4) the intersection with spatial units; and finally (5) the quantification and cartographic visualization of the building structure and dynamic. Applying the workflow to German authoritative geodata, it was possible to describe the entire building stock by 48 million polygons at different scale levels. Approximately one third of the total building stock are outbuildings. The methodological approach reveals that 62% of residential buildings are detached, 80% semi-detached and 20% terraced houses. The approach and the novel database will be very valuable for urban and energy modeling, material flow analysis, risk assessment and facility management.

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Automatic classification of building types in 3D city models

Cited by 55 publications

References 25 publications

An improved LOD specification for 3D building models

An improved LOD specification for 3D building models

Estimating Building Age With 3d Gis

A Workflow for Automatic Quantification of Structure and Dynamic of the German Building Stock Using Official Spatial Data

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