=Paper=
{{Paper
|id=Vol-2636/05paper
|storemode=property
|title=A GIS-based ontology for representing the surrounding environment of buildings to support building renovation
|pdfUrl=https://ceur-ws.org/Vol-2636/05paper.pdf
|volume=Vol-2636
|authors=Maryam Daneshfar,Timo Hartmann,Jochen Rabe
|dblpUrl=https://dblp.org/rec/conf/ldac/DaneshfarHR20
}}
==A GIS-based ontology for representing the surrounding environment of buildings to support building renovation==
https://ceur-ws.org/Vol-2636/05paper.pdf
Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
A GIS-based Ontology for Representing the Surrounding
Environment of Buildings to Support Building
Renovation
Maryam Daneshfar, Timo Hartmann, Jochen Rabe
Technical University of Berlin, Berlin, Germany
maryam.daneshfar@tu-berlin.de
Abstract. This research focuses on developing an ontology for representing
knowledge about the surrounding environment of a building in an urban context, consid-
ering the geospatial objects and processes such as built environment, vegetation, popula-
tion and so on. The ontology can be useful to create a knowledge management system for
different experts involved in the process of the building renovation, to extend the infor-
mation and stretch the domain from the individual building to the environment.
Knowledge about what entities and attributes to select is captured based on literature and
investigating the pilot demonstration sites. Such an ontology can help to structure the sur-
rounding data to support processes in different stages of the renovation. The final goal is
to support planners in decision making process namely in site planning and pre-data col-
lection phase, energy modeling, comfort analysis and so on to control cost and quality.
Moreover, it can be valuable in further studies of integrating data of various sources for
construction purposes.
Keywords: Ontology, Surrounding environment, residential building renovation.
1 Introduction
A rapid transition of urban areas towards energy efficiency is required mainly because
of the challenges that climate change creates [1]. Geospatial solutions and strategies
for energy monitoring management are needed to increase renewable energy usage in
urban areas. Core geographical data, thematic maps of environmental data and admin-
istrative data such as planning regulations are required to depict the building’s envi-
ronment. Energy-efficient building renovation is an inter-disciplinary task that covers
domains with different ontological outsets [2]. This research focuses on developing an
ontology for the surrounding environment in the building renovation process. The
intention is to provide a schema for surrounding data to extend the information and
stretch the domain from the individual building to its surrounding environment.
The focus in the building renovation process is usually on the individual building
and all the data collected are directly connected to the building or within a small radi-
us of the building. Geospatial science, on the other hand, focuses on all phenomena
Copyright © LDAC2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
which happen within a context connected to a specific location. Objects with specific
locations and attributes are spatially related to each other. Based on Tobler’s first law
of geography, "everything is related to everything else, but near things are more relat-
ed than distant things" [3]. Spatial processes, such as autocorrelation and interpolation
can provide valuable information about every individual object in the context [3].
Some studies address the significance of geospatial data in different construction pro-
jects [4], but their focus is on the implementation of BIM1 and GIS2 data integration
as two main sources of data for construction and geospatial domains. Göcer et al. [5]
focus on BIM GIS integration for building refurbishment. Aghniaey et al. [6] describe
how the weather data and shading of surrounding buildings affect the thermal comfort
analysis of the buildings. Nevertheless, to the best of our knowledge, there is no com-
prehensive study to conceptually define what kind of geospatial data are required for
the renovation workflow.
Ontologies as formal knowledge modeling, define a hierarchical classification of
concepts and their interrelations [7]. Ontology is an approach for “an explicit specifi-
cation of a conceptualization”, and conceptualization is the way of "thinking about a
domain” [8]. This paper aims to present an ontology that focuses on the surrounding
environment of a building. The geospatial domain is a broad field. How we are ob-
serving it in the building renovation process delineates the concepts and attributes
required in such an ontology. The goal of this ontology is to support planners for de-
cision making in the site planning and pre-data collection phase to control cost and
quality. Moreover, the surrounding environment is essential in other stages of the
renovation process, for instance in energy modeling and performance assessment of
the renovation design.
The paper is structured as follows: Section 2 presents the motivating scenarios for
this research. Section 3 describes the spatial model and how urban GIS ontology is
developed. Section 4 summarizes the methodology implemented for ontology specifi-
cation and knowledge capture and the ontology implementation for the surrounding
environment. Finally, the discussion and conclusions are followed in Sections 5 and 6,
respectively.
2 Motivating Scenario
The residential building renovation process in cities is happening in the urban context
and gets affected by all urban characteristics and processes in most stages of the reno-
vation workflow. Some studies address the effect of the surrounding environment in
the planning phase of the data collection. The surrounding buildings and vegetation
characteristics of the environment, as well as other spatial objects such as parking
lots, can affect the quality of the 3D scanning of the building [5].
In the detailed design phase of a renovation project, environmental data can direct-
ly affect the energy modeling of the building by integrating weather data coming from
1
Building Information Modeling
2
Geographic Information Systems
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
weather stations instead of using single sensors in the buildings which is more costly
[9, 10]. Solar masks of the building and shade effect by considering 3D geometrical
data of the surrounding buildings is another parameter in this phase.
Energy consumption in the urban context is affected not only by urban structure
and building use but also by the socio-economic profile of inhabitants [11]. Therefore,
the societal GIS data can be beneficial in this context as well. Different studies [12,
13] address the effect of built environment, roads, walkway, playgrounds, running
water and pools in the acoustic comfort of the building. Air quality, outdoor tempera-
ture, wind speed and direction, and many other environmental factors can affect the
comfort analysis of the building. Building’s height to road’s width ratio is used as an
indicator to find the density of the urban area. Dense areas (with a high value of this
ratio) can weaken approaching wind which reduces the air dispersion capability that
leads to less indoor air comfort [14]. Accessibility to renewable energy sources and
potential, energy, water, and smart grid network are all important in the phase of per-
formance assessment of the renovation design.
Despite all this, the geospatial domain and surrounding environment data have re-
ceived less attention specifically in the building renovation projects. Developing an
ontology for the surrounding environment in the building renovation process helps to
find out not only which knowledge system for the surrounding data can be considered
to amend the renovation process, but also, to envisage the possibility of integrating
the surrounding data with detailed individual building data. This can lead to a digital
twin that replicates the real-world into a system that models it as genuine as possible.
3 Spatial Models
This section aims at establishing an understanding of geospatial ontology develop-
ment and capturing knowledge in the geographical domain connected to the building
renovation. The important characteristic of geospatial data is that it ties attributes to a
location on the surface of the earth. The real-world is a very broad topic, and model-
ing and creating an ontology for such a system is a huge task. To this aim, this section
is added in order to clarify how models and ontology are developed in this domain.
There are specific issues in developing an ontology for GIS because of the com-
plexity, richness, and difficulty in the representation of geographical data [15]. The
geospatial domain tries to model the real world in order to simplify it for specific
applications [16]. Considering the real world as a physical model, how humans' per-
ception is, creates the cognitive model. Expressing human’s perception in a logical
way in order to move toward a computer-understandable approach leads to a logical
model. The logical model is categorized into two levels namely high-level and low-
level (Figure 1). The high-level modeling considers the general concepts of space,
while the low-level modeling focuses on specific concepts that belong to a particular
domain and task [17].
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
Expressing human's
perception in a an explicit formal structure
Real-world Human's perception Object view / Field view Algorithms / Raster, Vector
Physical model Cognitive model Logical model Representational model Computational model
General concepts High level Low level Specific concepts
Domain Task
Fig. 1. A paradigm for geographic world
The concepts defined in the logical model will be represented in the representa-
tional model, which categorizes reality in two basic categories namely object view
and field view [18]. Geographic objects are identified by their dimensionality. A point
is a feature with 0-dimension, a polyline is a feature with 1-dimension, a poly-
gon/surface is a feature with 2-dimensions, and a solid is a feature with 3-dimensions
[19]. An object view can be utilized to represent features such as building blocks and
roads. A field view, on the other hand, can represent the variation of specific phenom-
ena such as elevation and slope. The model which digitalizes the representational
model is called the computational model.
3.1 Ontologies in Urban GIS
This ontology focuses on the low-level logical model to target the concepts which are
in the urban GIS domain and applicable in the building renovation task. This logical
model can be used in future studies to represent the surrounding environment of the
building. Semantic mediators can be used to bring the logical model into the represen-
tational model and consequently into a digitalized model.
Based on [19], ontologies in urban GIS comprises of objects, such as buildings and
roads; processes, such as traffic flow and noise pollution; relations, such as buildings
belong to building blocks; events, such as traffic accidents (Figure 2). This approach
is used as a basis for developing an ontology for the surrounding environment in the
building renovation process.
Urban ontology
Objects Relations Processes Events
Fig. 2. Urban GIS ontology components
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
4 Research Approach
Based on the research gap described in the motivating scenario section, the principal
objective of this study is to develop an ontology to aid experts in the renovation pro-
jects to gain knowledge on possibility of utilizing geospatial surrounding data. The
research approach is the methodology which is used by France-Menah et al. [20] and
includes ontology specification, knowledge acquisition, conceptualization and imple-
mentation and validation. Ontology specification is done by answering questions re-
garding purpose, scope, intended end users, intended use of the ontology which can
be conceptualized in next steps.
What is the purpose? This ontology is developed to represent concepts related to the
surrounding environment of a building to support experts in different stages of build-
ing renovation projects.
What is the scope? This ontology includes real-world objects such as building and
road, as well as population-related, environment-related and energy-related processes
and their relations.
What is the intended end-user? The intended end users are site planners, data collec-
tors, energy experts and decision-makers who are involved in performance assessment
of the renovation process.
What is the intended use? The ontology in intended to be used as a knowledge man-
agement system to give a comprehensive view of all the objects and data layers which
are available in the surrounding of a building.
Some of the use cases which are developed according to literature, brainstorming
and experts’ opinions include site planning, building energy modeling, acoustic, air
quality, thermal and lighting comfort analysis. Site planning is mainly referred to all
the decisions made prior to the building data collection. 3D data collection of the
individual building which is under renovation is a time consuming and costly task.
Different methods are available for this task each of which requires its specific cir-
cumstances. Knowing in advance about the context where the building is located in,
helps to select the method for the data collection. For instance, assuming that, the
building is located in a high dense built area, or the façade is covered by single trees,
or considering the culture of the population living in the vicinity may convey that
drone-based data collection is not a good method for one specific site, and other alter-
natives such as terrestrial data collection is more practical.
Energy modeling occurs during the renovation design stage of the renovation
workflow. One of the key parameters in energy modeling is external weather data.
Historical and statistical weather data provided for energy modeling softwares are
usually old datasets. Using weather data services which provide more recent data can
help to get a more realistic model for the energy demand of the building. On the other
hand, the weather data is usually measured in rural stations. Urban context, because of
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
the urban heat island, experiences different weather situation from that received in the
rural station. So, another benefit of having information about the urban context is to
quantify urban heat island which results to urban weather and to calculate a more
realistic energy model for the building.
The knowledge about the use cases and the data required for them is captured
based on literature which implicitly accounts for surrounding data as an effective
factor. Moreover, investigating the surrounding environment of pilot demonstration
sites via aerial imagery, available maps and visiting the sites was another source of
knowledge. Some sources in literature on knowledge capture are summarized in Table
1. When the ontology requirements are specified, next step is to formalize and
conceptualize this specification. To this aim, a list of entities (objects) in a
hierarchical order are defined along with their attributes and their relations to the
processes.
Table 1. Use cases and sources for ontology knowledge capturing.
Use case Surrounding environment data Source
Building block, road, railway, vegetated area, Göcer et al. 2016
single tres, parking lot, monument, societal
Site planning
data …
Weather data (*.epw weather file for energy Crawley 1998
Building energy modeling modeling in EnergyPlus) Huang 2011
(BEM)
Road, walkway, railway, railway station, Herranz-Pascual et al.
Acoustic comfort analysis
airport location, playground and park, running 2017
water, pool, lake, noise map Kang et al. 2016
Leung 2015, Li et al.
Air quality comfort analysis Road, building , CO2 emission map 2009
Weather data, building, single tree, energy Aghniaey et al. 2018
Thermal comfort analysis consumption behavior of the occupants, re- Humphreys et al. 2000
newable energy sources
Lighting comfort analysis Building, tree Jung 2018 [24]
4.1 Ontology for Surrounding Environment
A sophistication in defining urban GIS ontology is the concept of bona fide objects
which address the visible objects in the landscape that are concrete in terms of physi-
cal boundaries and fiat objects which are not visible and do not have a physical border
[21]. This ontology tries to cover all the physical (bona fide) objects in the surround-
ing environment in the context of building renovation projects such as building, as
well as non-physical (fiat) objects such as district. The ontology also covers processes
that convey information about the distribution of specific phenomena in a location.
Both objects and processes are associated with some attributes and properties.
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
As shown in Figure 3, objects are categorized conceptually to the built area, vege-
tation, water, energy network, and traffic network. Each of the categories contains
different data layers which have different attributes and properties. A district is a built
area comprises of parcels, building blocks, buildings, parking lots and monuments. A
district has Area and Name attributes, a parcel has Area attribute as well as Landus-
eType. Residential and commercial are examples of land use. A building block con-
tains buildings that have attributes such as Area, Height, FacadeMaterial, Numberof-
Floors. A monument can have ConstructionRegulation constraint, for the buildings in
its surrounding which may include the buildings under renovation project.
Fig. 3. Object categorization in the surrounding environment of a building
Vegetation category contains park with Area and Name attribute and tree with
Height, CrownSize and TreeSpecies attributes. The energy network has Type attribute
to distinguish between different energy types such as gas and district heating. Water
category contains river, pond and lake with attributes such as Name, Width, Area.
Traffic network category comprises road, walkway, and railway with Width and Type
attributes. Additionally, it includes airport and railway station with Name and Type
attributes.
On the other hand, as shown in Figure 4 and Figure 5, processes are also catego-
rized conceptually in a similar fashion as objects. The main processes which can be
helpful in renovation projects are population-related, energy-related and environment-
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
related. The energy-related process components are energy consumption and renewa-
ble energy potential. The energy consumption process has Type to categorize the sort
of the energy consumption and has Value to define the amount of consumption for
each type. The renewable energy potential process is categorized to biomass, geo-
thermal, wind power and solar energy potential. The amount of biomass potential in
heat and electricity generation is defined by Value. The geothermal potential has
Depth which is the depth of the drilling points to extract the energy and has Value to
indicate the withdrawal performance. Wind power has ElectricityFeed that defines the
amount of wind power potential. Solar energy potential is categorized into solar ther-
mal and photovoltaic and determine the amount of heat and electricity feed with heat
and electricity Value.
Fig. 4. Energy-related processes in the surrounding environment
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
Fig. 5. Population-related and environment-related processes in the surrounding environment
The population-related category contains processes such as population age, densi-
ty, and education level. Population age has PopulationAgeRange to classify the popu-
lation age in different ranges and has Value to determine the share of each range.
Population density has Value to define the amount of density, and population educa-
tion level has EducationLevel to classify the population education in different catego-
ries and has a Value to define the share of each category.
The environment-related category contains noise level and CO2 emission sub-
categories that are defined by a Value. Moreover, it comprises weather sub-category
which is linked to WeatherFile. The weather file has a constraint of FileFormat that
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
can be changing according to the energy modeling software (for instance *.epw file
format for EnergyPlus software). District, building block and building can all have
relation with the processes, meaning that information about all these processes may be
available for each of these levels.
Representing this model in RDF/OWL standards is the best practice for storing vo-
cabularies and ontologies and publishing them on web and making the ontology
unique and available via URI. To this aim, the OWL of this ontology has been pub-
lished and is available3.
5 Discussion
Although many articles are focused on how to integrate BIM and GIS data semanti-
cally and practically, none of them address a comprehensive overview of what are the
surrounding GIS data, which can be utilized in general in an AEC project and in par-
ticular in the building renovation process. Some studies have already mentioned the
usage of surrounding data in some of the phases of the building renovation process,
but there is a lack of attention in many other studies of renovation workflows. This
topic is important because there are a lot of available geospatial sources of data which
can help to generate more accurate results in the building renovation workflow.
Hence, the first aim of this research is to provide this knowledge structure available
for experts in the renovation process.
Based on use cases, brainstorming, and incorporating experts, this study focuses on
developing an ontological representation of the GIS surrounding and environmental
data to support planners, decision-makers, and experts involved in the renovation
process. Increasing the number of interviews with experts who are both familiar with
geospatial data characteristics and energy simulation can help to define new use cases
where geospatial data are required in the renovation process.
Future activities include validation of the ontology to check the competency of the
ontology towards addressing the problem identified in the motivating scenario. In this
respect, a proof of concept of using this ontology for the renovation process will be
presented. Moreover, this ontology is developed in a modular pattern. The upper lev-
els in the ontology start with a conceptual element of built area, vegetation, and so on,
the concepts which can be defined in an urban context. Using this modular format, it
is also possible to connect this ontology with the existing and more wide-spread on-
tologies such as Building Topology Ontology (BOT). BOT contains the ‘site’ compo-
nent which based on its definition is about the site where for instance the residential
building is located in [22]. The site element can be expanded to the surrounding envi-
ronment where this ontology is defined in. On the other hand, as this ontology is
developed on a conceptual level, details regarding the geometry for these objects are
not included, although geometry is a relevant characteristic of all these objects.
3
http://dx.doi.org/10.14279/depositonce-10123
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Proceedings of the 8th Linked Data in Architecture and Construction Workshop - LDAC2020
Hence, considering the integration of geometry description via Ontology for
Managing Geometry (OMG) is considered as a future task [23].
With regard to the construction and geospatial domain, different open standards are
developed e.g. IFC and CityGML both focusing on the 3D model of the building and
built environment respectively. Investigating the possibility of using CityGML and
other existing standards as the basis for developing the ontology developed in this
research is considered as another future task.
Lastly, linked data is a concept related to interrelated datasets on the web, and it is
considered as the heart of the semantic web. A future task would be investigating how
developing this ontology and integrating it with other ontologies can contribute to the
linked data.
6 Conclusion
In spite of the fact that buildings in urban areas are located in the urban context, got
affected by those features and affect their surrounding during the renovation process,
geospatial domain has not got enough attention. This research provides a knowledge
system for the experts involved in the renovation workflow to bring into account
those datasets for site planning, energy modeling and finding best design scenarios.
This study is a work in progress, investigating how this ontology can be evolved
using other standards or incorporating components of other standards, also involving
other experts to define new use cases or investigating possibilities of using other geo-
spatial datasets in the already-mentioned use cases can be reflected in future devel-
opment.
Acknowledgement
This research project is funded under the European Union’s program H2020-NMBP-
EEB-2018, under Grant Agreement no 820553.
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