Using 3D GIS to estimate population and refine census maps

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, in our new paper we investigate to what extent they can be used for the same purpose:

Biljecki, F., Arroyo Ohori, K., Ledoux, H., Peters, R., & Stoter, J. (2016). Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands. PLOS ONE, 11(6), e0156808. doi:10.1371/journal.pone.0156808

Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach.

The paper is Open Access.

paper-cover_page

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An improved LOD specification for 3D building models

The CityGML 2.0 LODs are an industry standard for conveying the grade of 3D city models. However, the 5 LODs are not defined precisely, and they are not sufficient for this purpose. In our new paper

Biljecki, F., Ledoux, H., & Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment and Urban Systems, 59, 25–37. doi:10.1016/j.compenvurbsys.2016.04.005

we present a refined series of 16 LODs that overcomes these issues:

CEUS-LOD-1

The freely available author’s version PDF is available here. Please use the publisher’s version if available to you.

CEUS-LOD-2
Abstract: 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.

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The variants of an LOD of a 3D building model and their influence on spatial analyses

The level of detail (LOD) concept conveys the grade of 3D city models, however, it still allows flexibility for different modelling choices. For instance, consider the following four (valid) variants of LOD1:

ISPRS-GR-2

 

These variants, which we term geometric references, are a topic of our new paper which has been published in the ISPRS Journal of Photogrammetry and Remote Sensing:

Biljecki, F., Ledoux, H., Stoter, J., & Vosselman, G. (2016). The variants of an LOD of a 3D building model and their influence on spatial analyses. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 42–54. doi:10.1016/j.isprsjprs.2016.03.003

The freely available author’s version PDF is available here. Please use the publisher’s version if available to you.

ISPRS-GR-1

Abstract: The level of detail (LOD) of a 3D city model indicates the model’s grade and usability. However, there exist multiple valid variants of each LOD. As a consequence, the LOD concept is inconclusive as an instruction for the acquisition of 3D city models. For instance, the top surface of an LOD1 block model may be modelled at the eaves of a building or at its ridge height. Such variants, which we term geometric references, are often overlooked and are usually not documented in the metadata. Furthermore, the influence of a particular geometric reference on the performance of a spatial analysis is not known.

In response to this research gap, we investigate a variety of LOD1 and LOD2 geometric references that are commonly employed, and perform numerical experiments to investigate their relative difference when used as input for different spatial analyses. We consider three use cases (estimation of the area of the building envelope, building volume, and shadows cast by buildings), and compute the deviations in a Monte Carlo simulation.

The experiments, carried out with procedurally generated models, indicate that two 3D models representing the same building at the same LOD, but modelled according to different geometric references, may yield substantially different results when used in a spatial analysis. The outcome of our experiments also suggests that the geometric reference may have a bigger influence than the LOD, since an LOD1 with a specific geometric reference may yield a more accurate result than when using LOD2 models.

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Two papers at UDMV 2015: (1) conversion between CityGML and OBJ, and (2) visibility analysis in point clouds

My group is organising the forthcoming Eurographics Workshop on Urban Data Modelling and Visualisation 2015. I’ve co-authored two peer-reviewed papers that will be presented at the workshop:

 

Biljecki, F., & Arroyo Ohori, K. (2015). Automatic Semantic-preserving Conversion Between OBJ and CityGML. Eurographics Workshop on Urban Data Modelling and Visualisation 2015, Delft, Netherlands, pp. 25-30. [PDF] [DOI]

 

Peters, R., Ledoux, H., & Biljecki, F. (2015). Visibility Analysis in a Point Cloud Based on the Medial Axis Transform. Eurographics Workshop on Urban Data Modelling and Visualisation 2015, Delft, Netherlands, pp. 7-12. [PDF] [DOI]

 

The code supporting the first paper is available on Github.

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New IJGIS paper on the propagation of error in 3D GIS and in estimation of the solar irradiation

A new paper has been published in IJGIS:

Biljecki, F., Heuvelink, G. B. M., Ledoux, H., and Stoter, J. (2015). Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs. International Journal of Geographical Information Science, vol. 29(12), December 2015, pp. 2269-2294.
DOI: 10.1080/13658816.2015.1073292

The paper deals with the following:

  • Extension of the error propagation theory to 3D GIS
  • A method to determine the propagation of error with procedurally generated (synthetic) 3D city models
  • Development of a CityGML compliant software prototype to estimate the insolation of rooftops of buildings
  • Investigation of the propagation of error in the estimation of the solar irradiation

The code (Solar3Dcity) supporting this work can be found on the Github repository of our group.

 

ep-solar-cover ep-solar-disturbed

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New IJGIS paper: Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry

A new paper has been published in IJGIS:

Boeters, R., Arroyo Ohori, K., Biljecki, F., and Zlatanova, S. (2015). Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry. International Journal of Geographical Information Science, vol. 29(12), December 2015, pp. 2248-2268.
DOI: 10.1080/13658816.2015.1072201

The paper deals with the following:

  • Definition of LOD2+: indoor geometry with an LOD corresponding to LOD2 (CityGML).
  • A method to automatically generate LOD2+ data from models with an exterior geometry.
  • Tests with real-world data: we have generated the LOD2+ of Rotterdam, from the CityGML dataset Rotterdam 3D.
  • Use case: calculating the net internal area of buildings, and validation with real world data from government records.

The code (lod2plus) supporting this work can be found on the Github repository of my group.

 

cover

 

 

 

lod2p

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UDMV 2nd call for papers and a LiDAR contest

The deadline for the UDMV 2015 workshop has been extended to 20 July. Please find the 2nd call for papers on the website of the event, or as a PDF.

Further, the UDMV 2015 point cloud contest has just been announced. It is aimed at researchers in the LiDAR, 3D modelling, and visualisation domains, but we also welcome a wide variety of other topics. Please find more information at the webpage of the contest. Note that the submission of abstracts is due on 16 October, and that the participation does not mandate attending the workshop. It is open to everyone.

The 3rd Eurographics Workshop on Urban Data Modelling and Visualisation is a multi-disciplinary event that bridges 3D GIS and computer graphics, with two successful editions organised in the past (in France and in Spain). It will be hosted on 23 November by the 3D geoinformation group at TU Delft.

We are looking forward to your valuable submissions.

flyer_smallwindmill1

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A list of free/open CityGML data

Some time ago I’ve started to compile a list of CityGML open data initiatives. There are not many freely available 3D city models stored in CityGML, so it’d be a good idea to maintain an inventory. The list can be found at the CityGML wiki.

If you are aware of a dataset not listed, please let me know.

Open_Data_Initiatives_-_Citygml

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Teaser of Solar3Dcity

I have created a brief animation of the results computed by my recently released software Solar3Dcity:

Feel free to use it around to showcase a 3D use-case. Estimating the solar potential of roofs with 3D city models is one of the prominent 3D use-cases, and it’s always nice to show it as an example. Partly because this application is not possible with 2D data, but also because it is becoming wide- spread.

The software prototype is available at Github.

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An update on Random3Dcity

It has already been two months since I have released Random3Dcity, an experimental basic procedural modelling engine that natively supports CityGML. It is an experimental research prototype that I have built for my PhD project, but nevertheless it has generated quite some interest in the 3D GIS community.

For instance, it has been featured on the ISPRS datasets webpage and in the ISPRS newsletter of December 2014. Nice promotion.

Another thing worth mentioning is that my friend Marko Čubranić found it interesting and he has decided to 3D print a sample dataset. I guess that goes to history books as the first 3D printed CityGML procedurally generated model 🙂

3D printed CityGML dataset with Ultimaker II. Sample of Random3Dcity.

3D printed CityGML dataset with Ultimaker II. Sample of 144 gridded buildings with a street network generated with Random3Dcity.

The most important news is that I have updated the code so now it works with Python 3 (thanks to Mickaël Brasebin for the suggestions). I have also squished a bug where an lod3MultiSurface could have been composed of multiple gml:MultiSurfaces, which is a violation of the CityGML 2.0 standard.

Bug reports and suggestions are always welcome.

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