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.


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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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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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Random3Dcity – the first CityGML procedural modelling engine and multi-LOD building generator


I am happy to announce the release of Random3Dcity, an open-source experimental CityGML procedural modelling engine that I have developed within my PhD research at the 3D geoinformation group at the Delft University of Technology. It is an experimental software prototype that was created for research purposes, but potential applications are not limited to it.

I have built Random3Dcity entirely from scratch with a custom grammar, and implemented it in Python. The source is available on Github. A prepared collection of sample datasets is available on the datasets page, with extensive technical details, so I invite you to visit it if you are interested in further details and/or interested in the data without the need to run the software.

This is the first procedural modelling engine that generates buildings and other features in CityGML, and one that is designed to do so in multiple levels of detail. The engine generates buildings according to a novel series of 16 refined levels of detail (“Delft LODs”) that I have developed during my research on this topic:

This specification will be detailed soon in a research paper that is currently under submission. The program supports five types of roofs:


The number of unique buildings is virtually unlimited, and the datasets are suited for a number of application domains, from error propagation analysis to the testing of validation and repair software.

Random3Dcity supports interior (see the image in the header), and also vegetation and roads:


Further, the engine generates different geometric references within each LOD (e.g. LOD2.0 with the walls at their actual position and another [photogrammetric] LOD2.0 with the walls as projections from the roof edges), and different geometry (solid vs. b-rep). This results in almost 400 representations of a building. I believe that this is the most thorough CityGML dataset available to date. Solids are assembled by using the surfaces that define the usable volume of the building:


The composite rendering below shows an example dataset of 100 buildings in four LODs.


A research paper is under submission to the journal Computers, Environment, and Urban Systems, describing the engine and the refined LOD specification. I will update this post when the paper becomes available. If you are interested in using the engine, please contact me to give you the reference to cite.

For more information about this project please head to my personal page. Please let me know if you encounter a bug and/or have a suggestion. Note that this is an experimental software under continuous development.

As a bonus, check a video of a sample dataset of 10000 buildings:

Happy CityGML-ing!

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SciRev – reviewing the review process

Some time ago I have stumbled across SciRev, a very interesting idea of a website for sharing  experiences with the journal review process (“reviewing the reviews”).

The idea is that researchers share their quantitative experiences for publications they had submitted to a journal (incl. rejected papers), e.g. duration of the review, number of reviewers, and decision time. The averaged values for a journal would help other researchers to assess its suitability for a publication, and to set expectations.

Its database seems to be mostly empty (Science has only 5 reviews, while Nature has 2). But it’s a fairly new service, and it takes only a minute to add your experience, so I hope that this valuable initiative will become popular. I have added my recent experiences with journals in the GIS field, and there is no reason that you don’t add yours now. 🙂


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Our new 3D GeoInfo research group at TU Delft

My supervisors, a number of colleagues and me have moved to a new section within TU Delft: the 3D geoinformation research group. The website of the group is


I am relaying the announcement from the group’s website:

Since 1st of November 2014, our 3D geoinformation research group has started.

Our vision is that the reality is complex and constantly changing and therefore 3D geoinformation (outdoor and indoor) is extremely important to manage, predict and maintain the complex reality. We study and develop techniques to model, maintain, analyse and disseminate 3D geoinformation. Serving the nowadays users’ needs is thereby extremely important and therefore we develop solutions in close collaboration with users such as experts of noise, air and evacuation simulations. We are happy that our group is located within the Department of Urbanism, since 3D is a key aspect in the design and planning of interventions in the urban environment. We are therefore very close to the main users of 3D geoinformation who can advance in their own domain by using ours and vice versa.

Our group consists of three permanent staff members with different but completing expertise in the domain of 3D modeling: Jantien Stoter (3D geoinformation infrastructure), Sisi Zlatanova (indoor modeling and disaster management), Hugo Ledoux (data structures and algorithms for 3D modelling). Besides ourselves, PhD students and research visitors permanently work on highly relevant topics as you can see on our staff and projects pages.

We are looking forward to study and provide solutions for a solid and sustainable 3D basis to support existing and new applications!

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3D GeoInfo 2014

The conference 3D GeoInfo 2014 was held on 12 and 13 Nov in Dubai, UAE. It was organised by the Karlsruhe Institute of Technology and the American University in Dubai.

Me talking about my paper. Thanks Yan Zhou for the photo.

Me talking about my paper. Thanks Yan Zhou for the photo.

3D GeoInfo is an event that once a year brings together 3D GIS researchers, and this year it was particularly interesting because it was organised in conjunction with the 3D Cadastre workshop, which contributed to the diversity of the participants.

The presentations fit two full days, in a single session (yay!). The program was diverse, and it’s hard to tell if there was a central topic. An optional Oracle Workshop was organised a day before.

Proceedings are published in two collections:

  • A Lecture Notes in Geoinformation and Cartography book that will be available later in December.
  • Online proceedings hosted at the German National Library (freely available).

I have presented two papers, and I have used the opportunity to announce my procedural modelling engine Random3Dcity, which generated interest among CityGMLers.

3D GeoInfo 2015 will be held in Kuala Lumpur, Malaysia (hosted by UTM), with the call for papers already published. The deadline for full papers is 10 May 2015, and if you haven’t attended a 3D GeoInfo yet, I warmly recommend it.

Rumour has is that the 3D GeoInfo 2016 will be organised in Athens, Greece.

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My PhD project is featured in GIM International

My PhD research has been featured in a three-page article in the November 2014 issue of the magazine GIM International. If you have the received the magazine, you can find the paper at pages 21-23. Hopefully the online version will be available soon, and I will update this post (update: the paper is available on GIM’s website).

GIM International paper

GIM International is the leading global magazine for geomatics. It reports the latest geo news and developments, and it is an important venue for the geo-practitioners.

I am impressed by the quality of the editorial process. If you didn’t do so yet, subscribe to their newsletter.

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ISO 19157:2013 and 3D data show that quality control standards might need their own quality control

A part of my research involves analysing and describing the propagation of errors in 3D GIS. This is a new field that has first been tackled by my paper presented at the ISPRS conference in Toronto (Oct 2014). The work done so far is limited to 2/2.5D, and the increased production of 3D city models involves some new aspects that are not available in 2/2.5D. For instance, the level of detail, which is one of the principal aspects of a 3D geo-dataset.

The quality of geo-data has been an important topic for a long time, and it has been formalised by the geo-information technical committee ISO/TC 211 in their standards. The ISO 19157:2013 Geographic information — Data quality is the principal standard for describing the quality of geo-data, and has superseded the ISO 19113:2002. Among other things, the standard deals with the evaluation and description of the positional and thematic errors, and the completeness of the data.

However, it seems that the standard falls short when dealing with 3D data and opens many questions:

  • It does not provide a way how to describe the quantity of invalid solids in the datasets, e.g. that 5% of solids in the 3D dataset are not valid. The Logical Consistency / Topological consistency element does not foresee that, and I see no other (or a generic) element that would fit this quality aspect.
  • It is not clear how to specify that the dataset contains data in the wrong level of detail. E.g. that the metadata says LOD2, but the dataset is actually LOD1. Is it something that can be stored in the usability element, the mysterious sixth quality element (see more about it below)?
  • The same question applies to the geometric reference of the model. E.g. the metadata states that the walls of buildings are reconstructed as projection from roof edges (the photogrammetric way), but the walls in the dataset are reconstructed as extrusion from the building footprint (cadastre data)? How would it be possible to note that such metadata is wrong?
  • The positional error in the height in the way that is described in the standard seems to be focused towards 2.5D data, i.e. digital terrain models, and it is not suited for 3D city models.
  • What is exactly the sixth quality element—the usability element? Are there any examples from practice, especially about 3D data? The standard seems to be indistinct about it.

I am really curious how do national mapping agencies deal with describing the quality of their 3D data since they usually rely on this standard which is, as shown above, not compatible with 3D.

I have contacted a few people about the these ambiguities, and the questions are still unanswered. If you have any insight about this topic, please let me know. The title of this post might be too bold, but I find it hard to believe that a geo-information standard released in 2013 does not regard 3D geo-information. This should be a priority in the next revision of the standard.

This might be an interesting MSc topic, so feel free to contact me if you are interested in investigating and solving these issues.

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My MSc topics in Geomatics/GIS (TU Delft)


It is that time of the year when master students are choosing the topic of their graduation project. At TU Delft the MSc thesis is credited with 45 EC, which corresponds to around 9 months of work. It is a demanding, but also exciting endeavour, that often results in a journal paper. For example see my MSc thesis, and the journal paper in the International Journal of Geographical Information Science.

For this season, I have announced six MSc topics in GIS and 3D city modelling which can be found here. They are general, and can be adapted to one’s interest and background.

If you have stumbled across this page from another university, and if you are interested in one of the topics do not hesitate to contact me.

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