Document [original]

Advanced Engineering Informatics 52 (2022) 101591

Available online 20 March 2022

Full length article

An ontology to represent geospatial data to support building renovation

Maryam Daneshfar

, Timo Hartmann, Jochen Rabe

Technical University of Berlin, Gustav-Meyer-Allee 25, 13355, Germany

ARTICLE INFO

Keywords:

Geospatial data

Building renovation

Knowledge framework

Ontology

ABSTRACT

Energy-efficient building renovation is an inter-disciplinary task and requires investigation about the building

condition in the urban, environmental, and societal context. Existing literature implicitly mentions the effect of

surrounding data in different stages of building renovation. Nevertheless, no conceptual framework is available

for practitioners to realize the potential of such data in specific phases of the renovation.

The main goal of this study is to understand: (1) based on what knowledge framework surrounding geospatial

and environmental data can support building renovation projects, (2) if developing an ontology can help rep-

resenting this knowledge framework, and (3) how experts and engineers involved in the renovation process can

contribute to development of this knowledge framework. The results present an ontology that maps surrounding

geospatial and environmental concepts for different renovation tasks and use cases within building renovation.

The ontology is built upon knowledge captured from previous studies that implicitly mention the effect of these

datasets in building renovation, as well as expert knowledge, brainstorming, and monitoring construction sites.

Additionally, a semi-structured verification and validation workshop has been performed to incorporate insights

from experts directly involved in different stages of building renovation process.

This paper contributes to the body of knowledge by generating a common framework for the surrounding data

required in building renovation. It has an implication in practice for engineers by providing a shared knowledge

framework and for software developers by providing a basis for BIM (Building Information Modeling) and GIS

(Geographic Information System) data integration for renovation purposes.

1. Introduction

A ‘Climate Neutral Europe by 2050′is one of the actions at the Eu-

ropean level, where policies are targeted toward increasing building

renovation rates and depth of energy saving in the renovation process

[1]. Energy-efficient building renovation is an inter-disciplinary task. It

needs to cover domains with different ontological outsets, such as

contextual, environmental, and societal data [2]. Investigating the sur-

rounding geospatial and environmental data can help to highlight the

impact of some of these factors. Experts in the architecture, engineering,

and construction (AEC) domain apply BIM and GIS data integration, as a

common practice, to benefit from the geospatial datasets in construction

projects. Sani et al. [3] and Zhu et al. [4] have carried out an extensive

review of these studies. For building renovation [5], scrutinized the

effect of surrounding buildings, vegetation, and parking lots in the

quality of building data collection. In addition [2], generated a frame-

work to assess the building renovation performance. The framework

includes datasets from different fields, including the geospatial domain.

Nevertheless, they do not explicitly mention the required surrounding

geospatial data for building renovation.

Today, municipalities devote significant effort to collecting geo-

spatial data for cities. As a result, voluminous amounts of geospatial data

for different locations in several levels of detail are available. However,

searching for geospatial data for a specific application from this pool of

data is overwhelming and requires expertise [6]. Having a framework

for managing geospatial datasets and realizing their potential in

different phases of building renovation is missing in existing renovation

studies. The first motivation of this paper is to fill this gap and provide an

overview of the required geospatial and environmental entities to sup-

port building renovation.

Ontology is an approach for “an explicit specification of a concep-

tualization”, and conceptualization is the way of “thinking about a

domain” [7]. There are different targets for developing ontologies. One

of them is supporting engineering design requirement capture to help

the experts in the domain communicate more conveniently [8]. The real-

world is a broad topic and modeling and creating an ontology for such a

* Corresponding author.

E-mail addresses: [email protected] (M. Daneshfar), [email protected] (T. Hartmann), [email protected] (J. Rabe).

Contents lists available at ScienceDirect

Advanced Engineering Informatics

journal homepage: www.elsevier.com/locate/aei

https://doi.org/10.1016/j.aei.2022.101591

Received 8 September 2021; Received in revised form 8 March 2022; Accepted 11 March 2022

Advanced Engineering Informatics 52 (2022) 101591

system is a huge task. A common practice is to model the geospatial data

for a specific application and domain that narrows it down to the

required concepts. For instance, urban ontology prunes the geospatial

concepts and relations and keeps those required for urban analysis.

Therefore, the second goal of this research is to investigate whether

ontology development helps to generate this knowledge framework.

Creating such a knowledge framework is beneficial since it can be reused

in different renovation cases in various locations [6].

To develop the ontology, the first step is to identify the exact reno-

vation tasks, where geospatial data are required or beneficial, according

to the knowledge retrieved from previous studies. Based on that, rele-

vant concepts and relations are determined. The last step is to validate

the ontology against the use cases and within the scope of those specific

tasks with the tight involvement of experts and engineers. Hence, the

final motivation of this paper is to include experts and engineers

involved in the renovation process into the development of this

ontology.

The paper is structured as follows: Section 2 presents the research

background and motivation. Section 3 summarizes the methodology

utilized for developing and evaluating the ontology. Section 4 presents

the results including the ontology implementation and evaluation.

Finally, the discussion and conclusions are provided in Sections 5 and 6,

respectively.

2. Research background and motivation

2.1. Geospatial and environmental data in building renovation

Geographical data has been represented in the construction domain

for different purposes such as urban planning, emergency response,

mobility, and railway planning [9,10]. Within the context of building

renovation, many studies investigate the effect of surrounding and

environmental data in applications related to renovation tasks such as

building energy modeling, accessibility to the renovation site, acoustic

and thermal comfort analysis [11–17]. To practically integrate these

concepts into building information models, [5] introduced a pre-retrofit

model that performs a BIM and GIS integration strategy to combine data

to provide contextual information about the building under renovation.

However, this study does not enumerate the required contextual data-

sets. Costa et al. [18] developed a platform including an integrated

ontology-based District Data Model (DDM). The DDM is a data model

that semantically integrates data of building and urban scale required

for retrofitting. The urban data in this ontology includes the geometry of

the building envelope, and the geometric representation of urban ele-

ments such as green areas, roads, and city amenities. The authors do not

explicitly mention the required or beneficial data for the application of

building renovation. They believe that these datasets can have an indi-

rect effect on the renovation process. In this study, the developed plat-

form uses this ontology to collect data in IFC and CityGML format.

However, this study highlights that CityGML cannot help in structuring

all the necessary data. Therefore, other datasets were added to the

platform in the form of contextual data [18].

Researchers presume that BIM and GIS data integration is the

optimal solution in practice for providing the data flow between con-

struction and urban domains. However, they usually miss an interme-

diate phase in which required concepts should be specified [19]. This

necessities development of ontologies that can cover all essential con-

cepts for a specific task. Applying prevalent data models such as Cit-

yGML for building renovation is not valid since it does not contain all

required concepts for different applications in the building renovation

workflow. In addition, expert knowledge required for renovation is

missing to a great extent.

2.2. Ontologies in geospatial domain

To model geospatial data, Open Geospatial Consortium (OGC)

introduced different standards. LandInfra is a conceptual model for

representing infrastructure facilities such as roads and railways [20].

GML (Geography Markup Language) is an XML (Extensible Markup

Language) grammar for expressing geographical features [21].

IndoorGML is a GML application schema to represent indoor spatial

information [22]. CityGML is an application schema of GML for the 3D

representation of data. CityGML was developed to reach a common

definition and understanding of the basic entities, attributes, and re-

lations within a 3D city model [23]. It is widely used, and recently a

growing number of projects have generated CityGML models of different

cities. This model is also employed frequently in integration with IFC

(Industry Foundation Classes) in construction projects. IFC is an open

international digital standard description of the built environment.

Different parties in a construction project use it for exchanging infor-

mation. This model only focuses on the individual building model and

does not include surrounding information [24]. Another building in-

formation model is gbXML (Green Building XML), which aims at facil-

itating and enabling the interoperability between building design and

engineering analysis, such as energy simulation of the building. The

gbXML schema includes building information required for building en-

ergy modeling, such as thermal zones, and some surrounding informa-

tion, such as vegetation [25].

It is tempting to use 3D city models such as CityGML for urban ap-

plications. However, CityGML components do not provide sufficient

concepts for particular applications [9]. Currently, in the geospatial

domain, there is no representation that suits all applications due to

complexity of the domain [26]. Task-specific ontologies can address this

issue [27]. An ontology should be developed within a specific domain

and task that restrict the scope and universe of discourse. Besides, it

should be developed in close collaboration with stakeholders and

practitioners in the domain [28].

Spatial data modeling is investigated in different applications from

different perspectives. However, to the author’s knowledge, no study

applied an ontology-based approach for the building renovation appli-

cation. To narrow down the geospatial domain, we applied urban

ontology as the starting point, as we assume the building under reno-

vation is located in an urban context. Urban ontology categorizes the

urban-related features to objects such as buildings and roads; processes

such as population density; relations such as building block has build-

ings; and events such as traffic accidents (Fig. 1). In the ontology

domain, ‘object’ is a term of art that is considered as things, events, and

processes of all sorts [29]. In the context of this research, an object is a

‘spatial thing’ that comprehends boundaries of physical (such as build-

ing and road) or non-physical (such as district and zip code) features.

Therefore, it is analogous to the ‘CityObject’ concept in CityGML.

Thus, in the object view of our ontology, some concepts are inspired

by the concepts introduced in CityObject in CityGML, such as building,

vegetation, and water bodies (Fig. 1). However, sub-categories of these

concepts are customized to focus on specific concepts related to reno-

vation processes.

2.3. Identifying research gap and contribution

Based on previous studies mentioned in Sections 2.1 and 2.2, we

identified three research gaps. Firstly, a knowledge framework for

geospatial and environmental data is missing in building renovation

studies. Such a framework would help engineers and experts in the

renovation workflow comprehend the benefit and requirement of geo-

spatial and environmental datasets. However, majority of studies

implicitly mentioned it for specific applications related to building

renovation. Therefore, as a first contribution, we conducted a literature

review on these studies and identified the relevant concepts as basis for a

knowledge framework. As a second contribution, we developed an

ontology to represent this knowledge framework. Lastly, we evaluated

the ontology with experts and engineers. Hence, in a bottom-up

approach, we integrated the knowledge of renovation project

M. Daneshfar et al.

Advanced Engineering Informatics 52 (2022) 101591

practitioners into the ontology development.

In the next section, we explain the methodology utilized for devel-

oping the ontology and its evaluation.

3. Methodology

This section presents the methodology utilized for developing this

ontology. As depicted in Fig. 2, the process starts with a literature review

on the studies which mention the utilization of geospatial data in diverse

renovation tasks. We used these studies to acquire the knowledge for

developing this ontology. The next step is ontology conceptualization

and implementation, followed by a verification and validation step. We

verified the ontology through brainstorming with experts in a workshop

and consistency checking using the faCT++ reasoner available in

Prot´

eg´

e [30]. In addition, we validated the ontology against its targeted

purpose by conducting a workshop with practitioners and representing

the surrounding geospatial data for a case study in GIS.

3.1. Literature review

Some studies implicitly mentioned effective parameters from the

surrounding of the building in different analyses of renovation projects.

Nevertheless, we realized that it is challenging for the engineers and

practitioners to identify the most suitable geospatial datasets and

workflows in different phases of the renovation process, for instance in

the planning phase. Therefore, we conducted a survey on relevant

studies and collected an explicit list of renovation tasks and the corre-

sponding required geospatial concepts. We used this survey as a basis for

capturing knowledge to develop the ontology.

Among different available approaches for literature review, we

applied snowball sampling for selecting the articles. This method is

recommended when it is challenging to access subjects with specific

target characteristics [31]. Therefore, this approach is suitable, as it

helps to access those publications which do not explicitly mention the

subject of study. In summary, the procedure starts with a set of relevant

papers. The next round of articles is selected based on the title, abstract,

and references provided. This procedure continues until enough articles

are available [32]. Most of the papers are published in journals that are

focused on building in the built environment. This is expected, as the

topic is in the conjunction of building and its surrounding environment.

Most of the papers are from the year 2010 onward, although we did not

have any constraint in selecting the papers. We surveyed mainly the

articles through google scholar.

3.2. Ontology development

The research approach for developing the ontology includes

ontology specification, knowledge acquisition, conceptualization and

implementation [33].

3.2.1. Ontology specification

Ontology specification is done by answering questions regarding

purpose, scope, intended end-users and intended use of the ontology

(Table 1).

3.2.2. Knowledge acquisition

The literature review mentioned in Section 3.1 is the basis for

knowledge acquisition. After defining the ontology specification, an

initial list of intended uses including specific renovation tasks that can

be supported by this ontology is prepared according to the literature

review. For each task, a list of surrounding datasets that can be required

or beneficial is assigned. These specific tasks include site planning,

building energy modeling, acoustic, air quality, thermal and lighting

comfort analysis.

Subsequently, brainstorming and experts’ opinions as well as

investigating the surrounding environment of real demonstration sites

through aerial imagery, available maps, and visiting renovation sites

helped authors to formalize the knowledge. After the ontology re-

quirements are specified, the next step is to formalize and conceptualize

this specification. For this purpose, a list of entities (objects) along with

some attributes and processes are identified, and some relations are used

to connect them.

3.2.3. Conceptualization and implementation

The ontology presented in this research aims at covering all the

physical (bona fide) objects in the surrounding environment in the

context of building renovation projects such as building, as well as non-

physical (fiat) objects such as district [29]. The ontology also covers

processes that convey information about the distribution of specific

phenomena in a location, for instance, distribution of energy con-

sumption or potential of renewable energy sources in the surrounding of

a building. The ontology is developed based on the concepts in urban

ontology, and in the object view it is inspired by the concepts in Cit-

yGML. Therefore, existing standards and data models are considered

when developing this ontology. To this end, objects and processes asso-

ciated with some attributes and properties are used as the starting point.

3.3. Ontology evaluation

The evaluation of the ontology includes investigating its quality and

correctness. These perspectives are examined through consistency

checking of the concepts and axioms and their relations (verification),

and competency checking of the ontology for the purpose it is developed

(validation) [19,34]. Fig. 3 shows the evaluation effort in summary.

3.3.1. Ontology verification

We conducted workshops with participation of five construction

engineers researching ontology development in the AEC domain for

verification of the concepts and relations introduced in this ontology.

Fig. 1. Intersection of urban ontology and CityGML schema.

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Advanced Engineering Informatics 52 (2022) 101591

These experts help with the verification of the ontology because they

have extensive general background knowledge in ontology develop-

ment. Furthermore, each of them has developed ontologies for specific

tasks for construction purposes in individual research. In this workshop,

we did not focus on the instantiation of the ontology for a specific

project. Instead, we presented a general description of the concepts and

relations in the ontology. The experts discussed based on extensive

scrutiny on the concepts, relations, and the hierarchy between them.

Moreover, consistency checking of the ontology helps for a correct

interpretation, which can increase the quality of the ontology. In this

regard, ontology reasoning helps to find the conflicts in the knowledge

content [19]. We implemented the proposed ontology using OWL/RDF

language in Prot´

eg´

e (the ontology file is available online

) [30]. After

considering the comments from experts, we performed an automated

consistency check using the faCT++ reasoner in Prot´

eg´

e Version 5.5.0

[35]. Reasoners perform a consistency check verifying there is no

inconsistency in the concepts and relations.

3.3.2. Ontology validation

The second part of the ontology evaluation is to check the compe-

tency of the ontology for the intended uses for which it is developed.

This task is more complicated than verification for two main reasons.

Firstly, the evaluation requires a representation of the ontology within a

specific context and for a particular purpose. Secondly, the task for

which the ontology is used should be sufficiently complex [36]. One

approach is implementing a prototype based on the ontology. Using the

prototype, it is possible to establish a demonstration of the model to ask

experts and engineers about their opinion. It is also possible to ask the

experts to use the prototype to solve an engineering task within an open-

ended experimental setting, without formal structuring of the process

[19].

In this research, the validation of the ontology consists of a workshop

conducted to check if the ontology accomplishes specific tasks, and

fulfills the expectations mentioned in the intended end uses in ontology

specification (Table 1). A prototype is developed to demonstrate the

ontology. The experts had practical experience with the prototype to

retrieve data for a specific case study. In addition, to clarify how these

datasets can be helpful for the experts, we visualized the geospatial data

for a case study in ArcGIS. The main activities for the validation effort

include:

3.3.2.1. Preparation of the prototype. We developed a prototype based

on the ontology, that serves as a repository for retrieving and storing the

required geospatial data for building renovation projects. It is designed

based on a service-oriented architecture (SOA) framework for retrieving

the required geospatial data that adheres to the OGC standards of Web

Feature Service (WFS) for retrieving vector data. The retrieved data shall

be downloaded in Shapefile or GML format and visualized in a GIS

software. The prototype includes a list of use cases for which the

required geospatial data is suggested.

3.3.2.2. Selection of the participants for the workshop. For selecting the

workshop participants, it is crucial to consider what skills are required to

assess the ontology according to the intended end-users of the ontology

[19]. The recipients of the invitation were chosen based on that specific

expertise (Table 1) and were asked with direct invitations [37]. Based on

the availability, we invited four engineers involved in building reno-

vation. The experts work in building energy modeling, acoustic, air

quality, lighting comfort analysis, research, and development in build-

ing renovation field. These experts are involved in an EU research

project focused on residential building renovation (Horizon 2020 BIM-

Speed project [38]). The project participants are 23 international com-

panies and research groups working on 13 different demonstration cases

across Europe. Therefore, the selected experts are directly involved in

real building renovation projects and can reflect on the ontology

development from an operational perspective.

3.3.2.3. Practical experience with the prototype. In this step, we asked

each of the experts to work with the prototype. We asked them to select

the building location on the map, check the list of use cases, choose the

use case that is most relevant to their field of work, check the data list

that is suggested for the use case, and provide their ideas about the listed

concepts. We asked the questions in a semi-structured manner to allow

the possibility for brainstorming. Questions included but were not

limited to:

•For the available list of use cases, what datasets they would recom-

mend as required or helpful.

•For the mentioned concepts, what other attributes they consider as

required or helpful.

•If the hierarchy used to present this ontology is meaningful and

logical.

•What other use cases they recommend for utilizing the surrounding

data in the building renovation process.

The questions have been asked in a less structured manner, as sug-

gested by [19]. It helped to have a more open discussion which subse-

quently resulted in exploring new ideas to improve the quality of

Fig. 2. Procedure of developing the ontology.

Table 1

Ontology Specification.

Purpose This ontology is developed to represent surrounding geospatial

and environmental data to support experts in different stages of

building renovation projects.

Scope This ontology includes real-world physical objects such as building

and road, conceptual objects such as district, and urban processes

related to population, environment, and energy.

Intended end-

user

The intended end-users are renovation project practitioners such

as site planners, data collectors, energy experts, performance and

comfort analysis experts, and decision-makers

Intended use The ontology is intended to be used as a common knowledge

management framework. This framework will give a

comprehensive view of all the datasets in the surrounding of a

building that can support building renovation.

https://doi.org/10.14279/depositonce-12787

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Advanced Engineering Informatics 52 (2022) 101591

ontology.

3.3.2.4. Visualization of geospatial data for a case study in GIS. The

benefit of having access to the surrounding data collected from the

prototype can be more evident if the geospatial data are visualized on

maps. For this purpose, we visualized some of these datasets on maps for

a specific case study using ArcGIS software. Envisaging the building in

its geospatial context can help better comprehend its limitations and

possibilities. Besides, we asked the experts about their current experi-

ences for collecting such datasets in renovation projects. Subsequently,

we investigated what information can be revealed from the maps to help

in a specific use case in the renovation of a particular case study.

4. Results

4.1. Knowledge capture from literature review

The studies using geospatial datasets in renovation tasks are sum-

marized and provided online

. The renovation tasks are categorized into

site planning, building energy modeling, thermal, acoustic, lighting, and

air quality comfort analysis. We selected this list of renovation tasks

from an exhaustive list of use cases for building renovation that is

developed in the BIM-Speed EU research project [38]. The participants

of the use case development are from construction companies and

research groups and are involved in building renovation projects. From

this list, the authors selected those use cases for which they expect

surrounding geospatial and environmental data are required. The

following provides a summary of some of the studies mentioning

requirement of geospatial data in each of these use cases.

4.1.1. Site planning

It is believed that the premise for success of the future development

in the renovation projects is site planning [39,40]. Different studies

mention diverse aspects of surrounding datasets in site analysis and

planning such as building data collection [5], primary analysis for

building energy demand [11], logistics and planning for access of

workforce and material, safety [40–42], regulations caused by historical

preservation and interconnection within the heating network and

renewable sources of energy for energy supply management of the

building [12]. The surrounding geospatial datasets can provide infor-

mation for planning the project in advance and understanding the lim-

itations and facilities on the construction site.

4.1.2. Building energy modeling (BEM)

BEM is one of the critical analyses in the building renovation process.

Environmental data, and particularly weather data provided from

weather stations, can directly affect the energy modeling of the build-

ings [13,41]. The shading effect of the surrounding obstacles, such as

buildings and trees, is another considerable parameter. For instance, one

study which evaluated the impact of tree shades on the building’s energy

demand shows a considerable reduction in energy use in the summer

Fig. 3. Ontology evaluation effort in summary.

https://doi.org/10.14279/depositonce-12787

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Advanced Engineering Informatics 52 (2022) 101591

season [43]. Also, altitude, street geometries, vegetation, and water

bodies that cause evaporative cooling can affect the local weather con-

dition [44,45,46]. In addition, energy consumption in the urban context

is affected by the socio-economic profile of inhabitants [44,47,48].

Consumption schedule in the building is directly affected by the con-

sumption behavior of the occupants.

4.1.3. Comfort analysis

It is essential to find effective factors in studying the occupants’

comfort from different aspects, as it is connected to the health issues and

well-being of the building occupants [49]. Different studies address the

effect of the surrounding built area, roads, walkways, playgrounds,

running water, and pools in the acoustic comfort of the building [44,47].

Different urban objects can affect the urban soundscape in the built

environment, such as playground zones because of children’s voices,

trees, vegetated areas, and pools, due to running water, footsteps, roads,

and walkways on account of traffic, and human voices [50–54]. Col-

lecting this information in the early stages of a building renovation from

geospatial data sources provides valuable information for understanding

the possible sound sources in the built environment. These datasets

deserve great attention in renovation projects since correct insulation of

facades or replacement of windows can considerably improve indoor

acoustic comfort [53]. Other issues such as air quality, outdoor tem-

perature, wind speed, and wind direction are also affecting the comfort

of the residents and they are all considered as external features. Build-

ing’s height to road’s width ratio is used as an indicator to find the

density of the urban area. Dense areas (indicated through high ratio

values) can weaken the wind circulation that reduces the air dispersion

capability, which leads to less indoor air comfort [53–56]. Decrease in

indoor daylight availability due to the external obstructions may in-

crease building heating and lighting energy demand [57].

It is also important to mention that understanding the availabilities

of the heat supply at the district level, and taking advantage of utilizing

these sources, along with reducing heat loss through a careful design of

the building envelope, leads to thermal comfort of the occupants

[53–55,58]. Surrounding building height and the façade material that

cause shading effects impact the interior lighting of the building and the

visual comfort of the occupants [52,53,57]. The next section provides a

detailed description of the concepts and relations in the ontology.

4.2. An ontology to represent surrounding environment of a building

As mentioned before, urban ontology is used as a basis for developing

the ontology in this research. Object and Process are the concepts

retrieved from urban domain and expanded in the direction which is

required for renovation task. On the other hand, to account for the urban

context, for the Object concept, a top-down bird‘s-eye view is applied to

categorize the entities. The bird’s-eye view is the view which is used to

represent geographical features on the maps and aerial images [59].

Some components in CityGML are also the source of inspiration to define

the objects in the urban domain (these components are highlighted in

red in Fig. 4).

Any object on the surface of the earth hasGeometry to define the

representation of the feature. Geometry is an important aspect of geo-

spatial data as geographic objects are tied to space [60]. We did not add

further details of geometry, with the purpose of keeping the ontology at

the conceptual level. The first view from the top is the District which

includes ZipCode. On the lower scale, a Site i.e., an area with a specific

radius around the building under renovation is presented. A Site includes

different Parcels. Each Parcel is related to LandUseType with the object

property hasLandUseType (Fig. 4). Each Parcel may include five main

categories. These categories are the BuiltArea, Vegetation, Water, Ener-

gyNetwork, and TrafficNetwork. Each of the categories contains different

sub-categories and different attributes are assigned to them. Building-

Block and Monument are considered as BuiltArea. BuildingBlock has an

object property hasBuilding which connects it to the Building. Monument

has an object property hasConstraint which connects it to Con-

structionRegulationConstraint. It is important to know the construction

regulation of monuments and historical places since it can affect reno-

vation workflow. Sub-categories of BuiltArea have object property

hasAttribute which relates them to some specific attributes such as Area

and Name. Attributes such as Height, FacadeMaterial, RoofMaterial and

NumberOfFloor and Area are assigned to Building.

The ReferenceBuilding corresponds to the building under renovation.

This concept is included in the context of Site as a sub-category of

Building. A Buffer should be created around the ReferenceBuilding to

Fig. 4. Object view in the proposed ontology.

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Advanced Engineering Informatics 52 (2022) 101591

select the desired surrounding concepts. The Buffer has a Distance to

define in which radius the objects are required to be collected. All the

sub-categories under Parcel such as BuiltArea and Vegetation are con-

nected to Buffer with locatedIn object property.

Vegetation category contains Park, PlayGround, and Tree. Park and

PlayGround have Area and Name attributes, while Tree including

SingleTree and StreetLineTree has Height, CrownSize, and TreeSpecies at-

tributes. Water category includes River, Pond and Lake with Name and

Width attributes. The EnergyNetwork distinguishes between different

energy sources such as gas and district heating by EnergyType attribute.

The TrafficNetwork category comprises Airport, Road, Railway, Walkway

and Station with Width, Type and Name attributes, and Parking consisting

of ParkingLot and ParkingSpace with Name and Area attributes. Some

attributes such as Height and Area are extended with two data properties

namely hasValue and hasUnit for more description. Although, same

approach is not used for multivalued attributes such as FacadeMaterial

and EnergyType, as the information related to them is not in the scope of

this study.

Fig. 5 shows the categorization of different Processes. The main

processes which can be helpful in renovation projects are Pop-

ulationRelated, EnergyRelated and EnvironmentRelated processes. The

PopulationRelated process includes those processes which are relevant to

the people living in the urban context such as PopulationAge, Pop-

ulationDensity and PopulationEducation. TrafficFlow is also considered as

PopulationRelated process, as it is defined as the interaction of pedes-

trians and travelers (i.e., people) in the traffic network. Therefore, it is

also connected to some entities in TrafficNetwork object. Environ-

mentRelated process includes particulate matter distribution (PMDis-

tribution), CO2Emission, UndergroundTemperature, NoiseLevel,

ClimateZone and WeatherData. The appropriate weather data for build-

ing energy modeling requires to include at least six parameters namely

dry bulb temperature, relative humidity, wind speed, wind direction,

direct and diffuse solar radiation.

EnergyRelated processes include EnergyConsumption and Renew-

ableEnergySource. RenewableEnergySource includes WindEnergy, Bio-

massEnergy, GeothermalEnergy and SolarEnergy. Photovoltaic and

SolarThermal are sub-categories of SolarEnergy. The hasFeed relation is

used to connect different renewable energy sources to ElectricityFeed and

HeatFeed, which are two entities used to define the potential of the

renewable energy sources. In addition, GeothermalEnergy is related to

Depth with hasDepth object property, to define in which depth, the

HeatFeed is provided. Information about all the processes mentioned can

be provided in District and ZipCode or even BuildingBlock and Building

level.

4.3. Ontology verification

We documented the discussion of the participants of the workshop

for verifying the ontology. Based on that, we recognized a list of de-

ficiencies and recommendations (Table 2), and modified the ontology

based on that.

Finally, with the help of faCT +reasoner, we discovered no incon-

sistency in the concepts and their hierarchy in the ontology (Fig. 6).

There are different purposes for using a reasoner including consistency

checking, classification, and realization of an ontology [61]. In this

research, we did not use the reasoner for classification and instantiation,

but only for checking if there are any contradictory factors in the model.

4.4. Ontology validation

4.4.1. Exploring the prototype

In the validation workshop, the experts worked with the prototype.

Each expert selected a use case that was most relevant to their field of

work and explored the concepts suggested for that (Fig. 7). Then, they

provided their suggestions related to missing concepts, relations, or any

other consideration.

Based on the comments from the participants, the validation of the

ontology resulted in the inclusion of some new concepts and relations,

that were missing. The experts did not have suggestions for adding new

use cases or specific renovation tasks, and the hierarchy of the concepts

and their relations seemed logical to them. Regarding the necessity of

having such an ontology, surprisingly, some experts believed there is no

requirement for such an ontology in building renovation projects,

although most of the others admitted that this ontology is beneficial.

They pointed out that the ontology is concise and provides an over-

arching framework for required geospatial data while it is not

Fig. 5. Process view in the Ontology.

Table 2

Recommendations and modifications from verification workshop.

Recommendations Modification

Include concepts related to the buffer

around the reference building

New concepts were added: Site,

ReferenceBuilding, Buffer, Distance.

New relation was added: locatedIn

Relate concepts to their attributes via

object property rather than data

property

In the first version, attributes were

assigned as data properties to entities. In

the updated version a new concept has

been created named Attribute. An object

property namely hasAttribute is utilized to

connect each concept to different

attributes.

M. Daneshfar et al.

Advanced Engineering Informatics 52 (2022) 101591

superfluous. The main points mentioned for each of the use cases are

summarized in the Table 3.

Based on the participants’ feedback, the authors scrutinized the

missing concepts and relations and included them in the ontology.

4.4.2. Validating the ontology for a case study

The ontology developed in this research aims at providing a

knowledge framework for geospatial information retrieval to support

building renovation. To validate the ontology, we tested it against a case

study. To clarify if the ontology can fulfill the specific goals that it is

intended to accomplish, we focused on employing the ontology for one

of the tasks, i.e., information retrieval for site planning. Using the pro-

totype, we downloaded some of the geospatial datasets suggested for site

planning for a specific case study in Berlin, Germany. Then we created

some maps in ArcGIS software to present them to the experts. We assume

it is an appropriate case study for this research, since (1) the building

location represents a real case scenario for building renovation, (2) the

site includes urban features such as surrounding buildings, road and

railway that can make building renovation a challenging task, (3) the

site is located in an active urban area, where the exterior situation of the

building can affect the building renovation from different perspectives.

Before exploring the maps with the workshop participants, we asked

them about the conventional approaches they use to examine the

renovation site before starting the project. One participant mentioned

that collecting information for investigating the construction site is

based on the data availability. A general practice before building reno-

vation is to examine existing 2-dimensional drawings, which may not

represent all the data layers of the building context. Another option is

using the ‘site plan’ of the area, which shows the existing and proposed

conditions of a given area. They usually include information about

transportation, utilities, vegetation, etc. Based on the available datasets,

the expert decides about the actions required before the renovation.

Another participant of the workshop mentioned that depending on the

size of the building under renovation, they may investigate the con-

struction site and possibilities for the equipment, accessibility of water

and electricity, etc. The first step to collecting such information is always

visiting the site. Although, the expertise and knowledge of the engineer

determine the topics to consider in the site survey. The procedure

Fig. 6. Result of the faCT++ reasoner.

Fig. 7. The prototype implemented based on the ontology.

M. Daneshfar et al.

Advanced Engineering Informatics 52 (2022) 101591

mentioned by both participants suggests that the knowledge and

expertise of the engineers involved in a renovation project have a key

role in selecting the required contextual data. Therefore, a standard

procedure is not available to realize the concerns for site analysis and

planning of the area and to have the knowledge framework to collect the

required datasets.

By developing this ontology, we introduced a knowledge framework

that helps engineers investigate the urban context of the building. How

to interpret these datasets is beyond the scope of the ontology. The

ontology only provides the knowledge framework for interpretation for

the experts. Based on the information retrieved from these datasets, the

experts can interpret and decide on better site analysis and planning.

As mentioned, to clarify the impact of the suggested geospatial

concepts by the ontology, we visualized some of the surrounding data on

maps and presented them to the experts. Some of the maps for this

specific case study are shown in Fig. 8.

The experts believe that the maps show that the building is located in

an area covered by historical objects. Therefore, it is essential to

consider possible limitations for the construction. Furthermore, the

building is surrounded by major and minor roads as well as a railway.

Therefore, acoustic analysis of the building is an essential task. Also,

information about the roads in the surrounding area can help for per-

forming activities such as logistic analysis, construction material and

work force accessibility. Moreover, solar thermal potential in the zip

code level and solar photovoltaic locations in the building surrounding

provides information about alternative energy sources for the building.

The experts believe that the suggested concepts by the ontology provide

the possibility for improving the site analysis. They mention that the

ontology fulfills the purposes, namely information retrieval and

providing a knowledge framework for the specific task of site planning

in the building renovation.

5. Discussion

The main questions that motivate this study are: (1) based on what

knowledge framework surrounding geospatial and environmental data

can support building renovation, (2) If developing an ontology is helpful

Table 3

Comments from the experts in validation workshop.

Renovation

task

Missing concepts Concerns

Site planning Orientation of the building,

climate zone

District level information are

more beneficial in planning

stage.

Building

energy

modeling

Underground temperature Information about energy

sources is useful in connection

with information about culture

and population age (societal

data).

Lighting

comfort

analysis

Radius of the buffer around the

building, height, façade

material and roof material of

surrounding buildings

A simple extrusion of building

can be enough (required), but

information about façade

materials in buildings can

improve analysis (beneficial),

the radius of the buffer for data

collection is important when

studying the surrounding

lighting effect.

Acoustic

comfort

analysis

Traffic flow, tree, buildings Some of the experts think

ontology is not required for

acoustic analysis as they

believe each software

requirement determines the

necessary concepts.

Fig. 8. Maps of the surrounding data for Berlin renovation case study.

M. Daneshfar et al.

Advanced Engineering Informatics 52 (2022) 101591

to generate this knowledge framework, (3) How experts and engineers

involved in the renovation process can contribute to development of this

knowledge framework.

Researchers focusing on BIM and GIS integration promote it as an

optimal solution for providing the data flow between construction and

urban domain [62]. However, the expert’s knowledge is crucial in this

stage to determine the required concepts for specific applications of a

particular task. When this is clear, the entities and relations can be

represented in an ontology. There are different data models to represent

geospatial data such as CityGML which represents 3D features in cities

such as buildings, road, river, and vegetation. There are also some

models within the AEC domain which include surrounding data. For

instance, gbXML is a building data model to facilitate building energy

modeling. In addition to the building information, gbXML includes

surrounding data such as buildings and vegetation, as they can affect the

building energy modeling [63]. Even though CityGML in urban domain

and gbXML in energy modeling field are the focus of attention, none of

them fit suitably for the building renovation task. Therefore, this study

narrowed down the concepts and relations in the surroundings of a

building to a finite number of concepts and relations required for the

specific task of building renovation as suggested by [64].

To this end, the main contribution of this research is an ontology that

represents a comprehensive view of surrounding geospatial and envi-

ronmental data that can support building renovation in different phases.

The ontology comprises surrounding physical and conceptual objects,

processes i.e., the geospatial distribution of specific phenomena, attri-

butes assigned to these concepts, and relations used to connect these

concepts. The development of the ontology started by identifying a list

of renovation tasks and use cases from an available list of use cases from

an EU research project and in the light of existing literature. After

developing the ontology, we performed a verification and validation

workshop to analyze the ontology against those use cases. We also

suggested a tight involvement of practitioners and engineers in ontology

development, as proposed by [19]. Therefore, several practitioners who

are experts in building renovation participated in the workshops,

thereby forming, and expanding the knowledge framework. The experts

who participated in the validation workshop have acknowledged that

the proposed ontology can work as a common knowledge framework to

help engineers and decision-makers in the building renovation projects

control cost and quality.

One of the limitations of the study is the limited number of experts in

the validation workshop. Some outlook for future research includes

involving more experts from more diverse fields within the renovation

workflow to expand the perspective on this topic. Moreover, applying

different approaches in the workshop, such as gaming to have more task-

oriented and in-depth discussions are some of the activities foreseen for

future research. New ideas from other experts as well as adding new

articles to the literature review resources may lead to some alteration in

the concepts and relations of the ontology. Therefore, the proposed

ontology is an evolving knowledge framework, and it has potential for

expansion in the use cases, concepts, and relations.

Another future research topic is extending existing ontologies and

data models from the geospatial domain such as CityGML. We utilized

the concepts in CityGML in this research as a basis for developing the

ontology. Nevertheless, implementing a CityGML ADE (Application

Domain Extension) is a future research task. CityGML ADE is a mecha-

nism of CityGML that extends the data model with additional concepts

for particular use cases [65]. Using an acknowledged model such as

CityGML makes the BIM and GIS data integration more straightforward

in a potential next stage.

This paper suggests utilizing this ontology for the building renova-

tion application, but one of its limitations is that it does not demonstrate

all aspects of using the ontology and its application in any practical

project. A future task can focus on the instantiation of this ontology for

particular use cases. As mentioned before, reasoner has different ap-

plications including consistency checking, classification, and

instantiation, while we only employed it for consistency checking [61].

A future research topic includes using the reasoner for instantiation. In

addition, the scope of the ontology is limited to the surrounding con-

cepts. Therefore, another limitation of this ontology is the partial in-

formation about the building under renovation and possibilities for

including concepts related to sensors connected to the building. More-

over, there is no extended information about some properties of some

concepts such as façade material, roof material, energy types, road type.

Lastly, different design choices for relating concepts and their attributes

may lead to different acceptable alternatives for the ontology.

The topic of ontology is inaccessible, particularly to the practitioners

and engineers for whom it can be most helpful. For this reason, many

practitioners believe ontologies are not beneficial in practice. This study

claims that developing a knowledge framework in the form of ontology

provides an opportunity to bring a more holistic view of the requirement

of geospatial data in the renovation workflow in practice as well as in

current and future research. Furthermore, the proposed ontology helps

integrating practitioners’ knowledge from the engineering domains to

the conceptual field of engineering informatics.

The ontology has implications in practice for engineers involved in

building renovation and software development. For the former group, as

a tool for a common understanding about a particular domain, while for

the latter, as a basis for BIM and GIS integration. It also has an impli-

cation for research by demonstrating that ontology can be used to map

knowledge from the geospatial domain for the building renovation

tasks.

6. Conclusion

Building renovation is a multi-disciplinary task involving experts

from different fields, where most of them are not aware of the accessi-

bility and benefit of surrounding geospatial and environmental data. As

a result, most of the time, analysis is performed, excluding the impact of

context. This necessitates developing an overarching knowledge

framework that includes several renovation stages in a holistic manner

and reflects on the significance of surrounding features in the renovation

workflow. This paper presents this knowledge framework and contrib-

utes to the body of knowledge by developing an ontology that serves as a

common reference for different expert groups in renovation projects. It

helps practitioners in the construction domain to understand how they

can benefit from the data which describes the surrounding to improve

their analysis.

For developing the ontology, knowledge is acquired from previous

studies that implicitly mention the effect of surrounding data in different

stages of renovation process. It also includes brainstorming, obtaining

expert knowledge, investigating maps of real demonstration sites, and

visiting construction sites. To evaluate the ontology, we conducted a

workshop attended by expert participants involved in building renova-

tion projects, those supposed to be the end-users of this ontology. Their

comments and concerns have been integrated into the development of

the ontology. Nevertheless, ontology development is an evolving task.

Therefore, this ontology has potential for expansion by investigating the

concepts suggested by other experts or redeveloped using available data

models from the geospatial domain such as CityGML.

Declaration of Competing Interest

The authors declare the following financial interests/personal re-

lationships which may be considered as potential competing interests:

[This research project is funded under the European Union’s program

H2020-NMBP-EEB-2018, under Grant Agreement no 820553.].

Acknowledgement

This research project is funded under the European Union’s program

H2020-NMBP-EEB-2018, under Grant Agreement no 820553.

M. Daneshfar et al.

Advanced Engineering Informatics 52 (2022) 101591

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