Category Archives: Blog-en

3D city models CityGML to lead the energy transition

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City 3D digital models to share, view and analyze urban spatio-semantiques data, the scale of the material to the territory. Multidomaines, they are used for various applications: noise modeling, simulation of flooding studies behavioural and socio-demographic, energy epidemiology...
Where the "Building Information Models" (BIM) stop at the end of the garden, digital 3D city models take into account the phenomena of urban interactions, such as the urban heat island and the sharing of applications and energy production of the buildings.

Diagnosis of heat needs and identify priorities of renovation of 3D.

Diagnosis of heat needs and identify priorities of renovation of 3D.

Planners, municipalities and energy providers have information to decide and coordinate the best strategies, combining low CO2 emissions and profitable investments.
Complete diagnostic instruments, these models allow to identify the priorities of renovation building by building and predict their potential for energy savings. -Territorial climate energy plan - energy strategies can thus be planned across the city, combining a reduction in energy demand and the best use of the potential of renewable energy.

One of the main challenges of these numerical models of city is the collection and data sharing. The availability and quality of these data directly impact the reliability of the energy analysis. While 3D urban geometries can be generated automatically and precisely using laser technologies or photogrammetric, the collection of semantic information related to the buildings and their uses is more complex and laborious. She asked to come across many sources of information and to use more or less automatic data acquisition methods.
It is essential to have a model of digital city unique, open, multidomain and multi 'level of detail', which could serve as a platform for Exchange of data between different urban actors.
The CityGML standard meets these criteria. It is an international reference, already used to model settlements in France (Lyon, Rennes) and Europe (London, Stuttgart).

Original version of article: review of the Centraliens, jan. 2015.

Treatment and harmonization of data: the right recipe

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Urban analysis is a long string of information, treatment of data collection (on the ground, import of databases, crowd-sourcing…) to viewing the results integrated into a virtual city model. Information lost not being able to recreate itself, the quality of the results is limited by the weakest link in this chain. If it is a particularly critical link, it is treatment and harmonization of data, following the phase of data collection.
My friend Piergiorgio de Ferrara likes to compare this step in a recipe. Let me borrow this tasty analogy.

 

Excerpt from the pie with leeks recipe

A recipe is a methodology. It lists the ingredients, stating their quantity and unit of measures, and details how they should be prepared before be integrated with each other.

 

Leeks purchased on the market must be quartered lengthwise, washed, and then cut into section of 2cm long before being put in a saucepan suitable for realize a pie with leeks.

Similarly, the collected raw data must be made usable in the urban analysis, transformed and eventually corrected to match the expected format. The use of code-lists specifying acceptable values of the text settings (function of the building, type of owner) has an important role in the harmonization of data. For digital settings, a mastered uncertitude is critical to the quality of the data.

Pre-prepared items (kub Maggy) to replace the missing ingredients cheaply. Just as libraries of data, collecting data for benchmarking that will be used by default if the stage of data collection did not fully provide the data needed for urban analysis.
In addition to the necessary ingredients, ingredients additives can enhance the taste of the dish (spices, truffle chips!), just like the optional data, which integrated into the processing of data will improve the accuracy and realism of the results.

Finally, without kitchen utensils adapted to the quantity and the nature of the ingredients, point of salvation. Similarly, data processing tools are essential. Be they commercial (FME) or freeware/opensource (HALE), there is something for every taste, for every budget, more or less strong and sharp. Also need to know to use: cook it as the expert in data processing must have the necessary experience, so that nothing will be left to improvisation.

Open data, open mind: the example of Helsinki

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For 2011, the city of Helsinki has led an offensive all-out transparency and open data, opening more than 1000 sets of data to the public. It joined this to 3 neighbouring municipalities Espoo, Vantaa and Kauniainen, in order to gather a panel of data giving the whole impression of this metropolis of 1.5 million people (equivalent to the metropolis of Lyon).

Amongst the data made available by the city, there is the State of the traffic and transport urban in real time as well as the meteorological measurements and sound pollution, energy consumption of the buildings, socio-demographic data on the population or how the money is spent in the city. It is even possible to track the activity of the plows in real time and find out if they are spent today in front of our doorstep.

Since November 2016, Helsinki has even made available and can be freely used two models of 3D city covering the entire city:

In order to promote the use of these data models, 3d city model hackatons are now organized.

 

Model high resolution of Helsinki – source: City of Helsinki

Responsible for the management and publication of these data for the 4 partner municipalities, the Agency Helsinki Region Infoshare has developed API standardized to facilitate the use of these open data. In addition, it opens its offices every 2 weeks stakeholders to present the new sets of data available, chat with users and listen to their requests. The city of Helsinki encourage fervently the communities of developers of mobile applications, researchers and journalists to use its data, detailing where to find what data in what format, via its Portal Dev.hel.fi. It supports more good initiatives and occasionally gives a boost. The city has invested more than € 100,000 in 2013 and 2014 to achieve a digital 3D model of the city in CityGML (open standard) format, now freely downloadable.

 

But why so much effort to be determined to share its data, so that these money at the same time at exorbitant prices in the heart of Silicon Valley.

 

Helsinki – source: Jens Passoth

"Open data to the public can save money to a municipality, for example by displaying transparent costs or letting others view and analyze its spending" recounts Tanja Lahti, Director of Helsinki Region Infoshare. This sharing of data and knowledge is also a gold mine for the local IT companies that develop mobile applications related to the databases of the city. Ilkka Pirttimaa has developed the BlindSquare app to help the blind and sighted in their daily lives. This app shows the way to the next tram stop, communicating the next tram time, if it is late and how many minutes. The app knows also libraries that offer books in braille, and will guide the user over the floors (internally have been digitized by the city) to find the good book to the right RADIUS, or possibly go to the closest restroom between two chapters.  The blind of Helsinki became with this app much more independent, some even to guide their labrador!

 

Open data boosts also participatory democracy, citizens being in turn consumers and voluntary contributors of this data exchange. They can easily notify City Hall a faulty hardware or a deterioration in their street, taking a picture with their smart phone and posting it on the app Fix-my-street accompanied by a short commentary. Generally, City Hall officials will participate in the following days.

This open data represents a significant cultural shift. She is at present in many other countries impossible. However, do not believe that the Finland is a lax State in terms of protection of the data or compliance with the sphere private, on the contrary: all published data must have been anonymized. In addition, any person handling these data received training of the person and copyright law. Is perhaps also a matter of open-mindedness.

More info:

 

 

 

About Level of Details

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Imagine that you fly in a balloon. You are at an altitude of 2000 metres, a vast landscape spreads before your eyes. Below, you can see small geometric shapes as well as long lines corresponding to fields and roads. Then you go down to 1000 meters, and you realize that roads are too wide, and that "points" are moving slowly on.

Get off again, now at an altitute of 300 meters, you are right on the top of a crossroads, you can now see the 2 channels of each road, and then the white stripe of the stops. The points became separate cars that stop to give way.

Aerial views in a balloon

Aerial views in a balloon

At 50 meters from the ground, you see even by the glass roof the two occupants from one of these cars. You have however lost the big picture that you had when the balloon was higher.
That experience faithfully illustrates the concept of levels of detail (Levels of Detail in English, which the LoD acronym is often used), one of the basic concepts of digital city models.

The LoD are used to define the different representations of an object from the real world. They are a kind of cursor between detailed overview and focus.
In existing neighbourhoods, the LoD to adapt the digital model to the availability and resolution of the data collected. For example, it is impossible to model the shape of the roof of a building, if only its cadastral data are known. Several LoDs can coexist in a single digital model of city, to represent various buildings modeled with various resolutions.
The concept of LoD is also very useful for the new urban projects. In such a case, the altitude corresponds to the different phases of the development of the project: it starts with a ground plan of the firm urban planners defining the footprint and the average heights of buildings), then comes the conceptual design fixing the form of the buildings, and finally the detailed design specifying the different architectural and technical elements as well as domestic.

2 HFT Stuttgart building represented in 4 levels of detail CityGML

2 HFT Stuttgart building represented in 4 levels of detail CityGML

Levels of detail are used and defined in many standard of the building (IFC) and the city (Blom3D, Navtek) modeling, based on various factors (geometry, semantic, texture, type of objects). In the open standard digital city CityGML, detail levels are 5 (LoD0 to the LoD4), defined mostly by the complexity of their geometry (detail of roof, openings, inside):

  • LoD0 included fieldwork and the footprint of the buildings (often called 2.5 D model)
  • LoD1 models buildings like the boxes to cardboard, consequence of the extrusion of their surface on ground until their average height
  • LoD2 adds the roof structure
  • LoD3 details the position of the openings (windows and doors) on the facades and roofs and the potential structural and architectural exteriors (balcony, pre-toit)
  • LoD4 finally introduced inside the building (equivalent to BIM) modeling

The texture of urban objects is independent of the LoD in CityGML, even if it takes its meaning from the LoD2.

The freeware Random3DCity developed in Delft (Netherlands) by Filip Biljecki allows to automatically generate models of city synthetic mixing these different LoD based on different criteria (density of construction etc.).

Software Random3Dcity, mixing LoD1 to LoD3 - source - TU Delft

Software Random3Dcity, mixing LoD1 to LoD3 – source – TU Delft

It is interesting to consider these levels of detail based on their purpose, i.e. of their potential application to different urban analysis.

A digital model of city LoD1 will be more than enough for a mapping of noise, when a LoD2, which models the actual shape of the roofs, will be needed for a study of the potential solar and photovoltaics. Applications requiring the Interior configuration of the buildings (natural lighting, indoor navigation, etc.) will make them call for a LoD4. For modeling of heat of a building needs, a LoD1 will provide the minimum information, however the choice of a higher LoD (LoD2, or even LoD3 and LoD4 for multizone modeling) will impact on the accuracy of the results, in particular refining the calculation of solar gains and the distribution of heat transfer. Several studies have quantified this precision gain related to the LoD on concrete cases (LoD1/LoD2 5% for[Nouvel et Zirak], x % for…). Note that the quality of simulation data, and therefore the accuracy of the results, is not that of the LoD, but also the quality of semantic data.

The consistency of these levels of detail has been questioned in recent years, due in particular to the fact that they consider only the geometric complexity and semantics.

LoDs alternatives, based on a more consistent combination of level of detail geometric and semantic have been put forward.