110 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170212 

Color differences between Urban Digital 

Twin and the real city. 

Optimisations and compensations  
Maria Martone1, Tiantian Fan2 

1 Formerly Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, 

maria.martone@uniroma1.it 

2 School of Media and Art Design, Wuhan Donghu College, Wuhan, China, ftt0532@gmail.com 
 
Corresponding author: Maria Martone (maria.martone@uniroma1.it) 

 

 

ABSTRACT 

With the rapid development of science and technology, urban digital twins have gradually become an essential 

tool for city planning and management, integrating real-time data such as traffic and energy consumption, and 

transforming our cities into smarter and more responsive environments. By creating virtual digital twin cities, 

urban management operations can be monitored and optimised in a more efficient and accurate manner, 

opening up new scenarios also projected towards the evolution of new development models for a better quality 

of life. 

On the basis of these premises and above all considering color as a key element in the visual perception of a 

city, the aim of this contribution is to verify whether in digital twins cities retain their color peculiarities and what 

the methodological criteria of reliability might be for a new way of managing color, considering the new 

technologies available.  

Referring to some examples of digital twins of cities published on the web and in particular referring to the 

Helsinki project, the essay aims to investigate whether and to what extent there are color differences between 

the virtual and the real vision of the city and what can be, on the basis of the results collected, possible 

optimisations or compensations to consider the Urban Digital Twin as a documental source also in color 

management both in the field of urban design and redevelopment and in the survey of the state of places.  

 

KEYWORDS Urban Digital Twin, color management, digital urban colors, Color Validity Index 

 

RECEIVED 04/04/2025; REVISED 22/05/2025; ACCEPTED 23/05/2025 

 

 

 

 

 

 

mailto:ftt0532@gmail.com


Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

111 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

1. Introduction 

Digital twin cities (Urban Digital Twins) represent one of 

the most advanced technologies for urban management 

and planning. These models integrate not only three-

dimensional representations of buildings and 

infrastructure, but also real-time data streams such as 

traffic, environmental monitoring and energy consumption 

(Mazzetto, 2024). Through the use of technologies such 

as the Internet of Things (IoT) and Artificial Intelligence 

(AI), Urban Digital Twins offer comprehensive control and 

optimised city management, making cities increasingly 

intelligent and responsive to modern needs (Ogunsakin, 

Mehandjiev, & Marin, 2023). 

The use of Urban Digital Twins is successfully spreading 

not only in Italy, but all over the world. In Italy, for example, 

Bologna has implemented this technology to protect 

cultural heritage, manage tourism and develop smart city 

applications. Similarly, cities such as Berlin, Helsinki, 

Shanghai and Tokyo are experimenting with various 

models of Digital Twins for different purposes, including 

managing urban resources and responding to 

environmental and climate emergencies. 

However, an often-overlooked aspect of these models is 

the accurate handling of color, which is fundamental to the 

visual perception and architectural identity of a city. This 

study aims to investigate whether digital representations 

maintain the color fidelity of real cities and whether color 

can be considered a geospatial datum accurately 

represented in Digital Twins. Through the analysis of some 

examples of Urban Digital Twins, with a special focus on 

the city of Helsinki, investigations will be carried out on 

samples of buildings in order to propose a methodology 

that can also be applied in other urban contexts. 

 

2. Urban Digital Twins: the case of Helsinki 

With the advancement of technology, urban digital twins 

have emerged as key tools for city management and 

planning, offering unprecedented opportunities to optimise 

urban services and improve quality of life. These models 

not only represent buildings and infrastructure in 3D, but 

also integrate data from IoT devices, enabling real-time 

monitoring of aspects such as traffic, environment and 

energy consumption. Globally, cities such as Singapore 

and Dubai have demonstrated the effectiveness of Digital 

Twins in improving urban safety and sustainability (Fig.1), 

using scenario simulation and managing resources more 

efficiently (World Economic Forum, 2023). 

However, one of the most complex and often overlooked 

aspects of these models is accurate color management.  

 

Fig. 1. Examples of Digital Twins cities. 

Color fidelity is crucial for the visual perception and 

architectural identity of cities, as it directly influences the 

aesthetic appearance of buildings, the legibility of urban 

spaces and the psychological well-being of citizens. 

Previous studies have shown that color reproduction in 

urban digital twins is affected by numerous challenges, 

including variability in daylight conditions, reflectivity of 

building materials and wear and tear over time (Wurm et 

al., 2024). For example, variations in brightness during the 

day can alter color perception, while the ageing of building 

materials can introduce discrepancies between the real 

and virtual worlds (Zhang & Liu, 2023). In Italy, the use of 

digital twins is growing, with cities such as Bologna 

exploiting their potential for cultural heritage protection and 

tourism management. In addition, companies such as 

CGR SpA (Compagnia Generale Ripreseaeree) are using 

photogrammetry and remote sensing with multispectral 

sensors to improve the color accuracy of urban 

representations. This approach has proven its value in 

complex urban management, emphasising the importance 

of integrating color data into virtual models. 

Despite technological advances, the application of color 

management techniques to open urban spaces remains a 

significant challenge. This study aims to help fill this gap 

by introducing a methodology based on the Chromatic 

Validity Index (CVI) to assess and improve color fidelity in 

urban digital twins. With a special focus on the Digital Twin 

in Helsinki, five representative buildings were chosen to 

perform a color sampling of the main elements (Fig. 2). 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

112 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

 

Fig. 2. Digital Twin in Helsinki. Highlighted are the five 

buildings chosen as champions. 

(https://kartta.hel.fi/3d/mesh/ accessed on 30 September 

2024).  

 

Through a detailed analysis of the virtual color 

representation and a comparison with real scenarios, this 

paper analyses the current state of color fidelity and 

proposes optimisation strategies to improve the overall 

visual perception of urban spaces. Finally, a new 

methodological approach for analysing and documenting 

color in digital twins is presented, contributing to more 

precise and integrated color management in urban design 

and redevelopment (Parraga, Troscianko, & Tolhurst, 

2002). 

 

3. Objectives and survey methodology  

Helsinki is an example of excellence in the use of urban 

digital twins due to its accessibility and advanced 

technological application. The city is a pioneer in heritage 

protection, urban planning and intelligent transport. The 

publicly accessible Helsinki Digital Twin offers interactive 

features that simplify urban analysis for researchers. For 

example, the Urban Data model integrates unique 

identifiers (GMLID, RATU and VTJ-PRT) for advanced 

data flow management and provides details such as 

height, material and year of construction for each building. 

The choice of Helsinki as the object of study is relevant 

both for its scientific value and for the practical applications 

it offers. This study analyses color fidelity in digital urban 

models, focusing on discrepancies between virtual and 

real scenarios and proposing strategies to improve color 

management in Digital Twins. As it was not possible to 

carry out direct surveys, façade colors were analysed 

using Google Earth, keeping the methodology unchanged 

to ensure consistency of results. However, it is hoped that 

future analyses can be refined with field surveys.  

The specific objectives of the research include assessing 

the current state of color management in the Helsinki 

Digital Twin, identifying color differences between the 

virtual and real city and proposing strategies to improve 

color fidelity in digital models. 

The case study focuses on the Aleksanterinkatu district in 

central Helsinki and five main buildings: the Cathedral, City 

Hall, University, Stock Exchange Building and Olli Johan 

Lindroos Jewellers. Color samples were selected 

uniformly on the main façade areas (walls, roofs, window 

frames and doors) to ensure representativeness of the 

data. The color data of the façades were extracted using 

Adobe Photoshop, with RGB values collected from both 

the digital model and real images. Color differences (ΔE) 

were calculated to classify deviations, while the Color 

Validity Index (CVI) (Chen et al., 2018) measured overall 

fidelity. Optimisation strategies include color calibration, 

light simulation and implementation of automatic 

correction algorithms. In this study, the main focus is on 

hue, as it is a key element for visual perception and 

architectural identity. Variations in brightness and 

saturation, although recorded, are considered secondary 

due to their dependence on external environmental factors 

such as natural lighting and dirt accumulation. Hue, on the 

other hand, offers a more stable and relevant indication for 

our purposes. 

 

4. Workflow 

1. Virtual model data collection and pre-processing. We 

acquired color data from the Digital Twin 3D of the 

buildings located along Aleksanterinkatu Street using the 

Helsinki City Urban Model. These data provide a detailed 

virtual representation, which is essential for later 

comparison with real data.  

2. Collecting and analysing the color of the real model. 

Using color extraction software such as Adobe Photoshop, 

we sampled the colors of the building facades, including 

areas such as walls, roofs, window frames and doors. For 

each sample, RGB values were recorded, ensuring 

accurate and consistent color analysis. 

3. Comparison of virtual and real scenes. The color 

samples of the virtual model were compared with the real 

images extracted from Google Earth. This comparison 

made it possible to identify and analyse any color 

discrepancies between the digital models and the real 

buildings, helping to assess the color fidelity of the Helsinki 

Digital Twin. 

 

5. Analysis of color data and differences. 

Comparative tables 

Color data was analysed on the selected samples for each 
building from both the Digital Twin in Helsinki and Google 
Earth to which was added an analysis of the calculated 
color differences as follows (Schanda, 2007). 
First, the RGB color space was converted to XYZ 
according to the following formula: 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

113 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

 
The XYZ values were converted to CIELAB, a color space 

that better reflects human perception of brightness, 

chrominance and hue. The transformation formula is as 

follows: 

𝐿∗ = 116 𝑓 (
𝑌

𝑌𝑛

) − 16  

𝑎∗ = 500 (𝑓 (
𝑋

𝑋𝑛

) − 𝑓 (
𝑌

𝑌𝑛

)) 

𝑏∗ = 200 (𝑓 (
𝑌

𝑌𝑛

) − 𝑓 (
𝑍

𝑍𝑛

)) 

Finally, the converted CIELAB values are used to calculate 

the color difference, referring to the following formula: 

 

∆𝐸𝑎𝑏
∗ =  √(𝐿2

∗ −  𝐿1
∗ ) 2 + (𝑎2

∗ −  𝑎1
∗) 2 +  (𝑏2

∗ − 𝑏1
∗) 2 

 

Finally, a comparative table of the results of the color data 

analysis of the samples taken from both the Digital Twin in 

Helsinki and Google Earth was drawn up for each selected 

building, a description of which follows (Figs. 3 and 4). 

Based on the ΔE value obtained, one can classify the type 

of color deviation, indicating whether it is a shift towards 

lighter, darker, warmer or cooler tones. 

The Cathedral. The virtual façades show lower brightness, 

predominance of green and higher saturation than in 

reality. Specific differences: walls ΔE = 17.11 (less 

brightness, green tendency); roof ΔE = 70.87 (low 

brightness, reduced saturation); door ΔE = 58.33 (red-

brown tendency); window ΔE = 13.12 (slight yellow 

tendency). 

The Town Hall. The virtual model is darker and colder. 

Specific differences: wall ΔE = 46.41 (cold and dark 

shade); roof ΔE = 26.00 (slight cold shift); door ΔE = 

190.75 (marked shift towards light and cold shades); 

window ΔE = 7.87 (good fidelity). 

University. Good color fidelity with exceptions. Specific 

differences: wall ΔE = 2.18 (high fidelity); roof ΔE = 20.70 

(slightly lighter and colder); door ΔE = 21.16 (lighter and 

less saturated); window ΔE = 22.23 (colder and darker); 

column ΔE = 13.28 (slight darkening).  

The Stock Exchange Building. Moderate discrepancies 

with lighter and less saturated tones. Specific differences: 

wall 1 ΔE = 23.17; wall 2 ΔE = 20.13 (both less deep and 

warm); roof ΔE = 55.89 (lighter); door ΔE = 23.75 (colder 

and desaturated); window ΔE = 27.61 (lighter). 

Olli Johan Lindroos' Jewellery. Marked differences, with 

generally lighter and less warm tones. Specific 

differences: wall 1 ΔE = 22.74 (less saturated); wall 2 ΔE 

= 72.98 (significant loss of warm shades); roof ΔE = 

102.69 (light and cold); door ΔE = 36.12 (less intense); 

window ΔE = 55.69 (less warm and less authentic). For a 

clear visualisation of the treated color deviations, see 

Figure 5. 

 

6. Evaluation of the effectiveness of color 

management using the Color Validity Index (CVI) 

The Color Validity Index (CVI) is used to measure the 

degree of color fidelity between the virtual model and the 

real scene. The formula for calculating the CVI value is as 

follows:  

𝐶𝑉𝐼 =  
∑ ∆𝐸

𝑛
 

The lower the CVI value, the higher the color fidelity. The 

color validity index was calculated for each selected 

building on the basis of a number of samples, as follows: 

The Cathedral: ΣΔE = 17.11 + 70.87 + 58.33 + 13.12 = 

159.43; number of samples = 4; CVI = 39.86.  

The Town Hall: ΣΔE = 46.41 + 26.00 + 190.75 + 7.87 = 

271.03; number of samples = 4; CVI = 67.76.  

The University: ΣΔE = 2.18 + 20.70 + 21.16 + 22.23 + 

13.28 = 79.55; number of samples = 5; CVI = 15.91.   

The Stock Exchange Building: ΣΔE = 23.17 + 20.13 + 

55.89 + 27.61 + 11.59 = 138.39; number of samples = 5; 

CVI = 27.68.  

Olli Johan Lindroos' Jewellery: ΣΔE = 22,74 + 72,98 + 

102,69 + 36,12 + 55,69 = 290,22; number of samples = 5; 

CVI = 58,04. 

Evaluation of the results. The results show that the 

University has the best color fidelity with a CVI of 15.91, 

while City Hall and Jewellery show significant deviations 

(CVIs of 67.76 and 58.04). These results underline the 

need for specific optimisation strategies to improve color 

accuracy in virtual models (Fig. 6). 

 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

114 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

  

Fig.3 - Comparative tables of virtual and real color and color difference data for the Helsinki Cathedral, City Hall, 

University and Stock Exchange Building. 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

115 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

 

Fig.4 - Comparative table of virtual and real color and color difference data from the ‘Olli Johan Lindroos’ Jewellery 

Shop in Helsinki. 

 

Fig.5 -Chromatic deviations (ΔE) for architectural elements. The graph illustrates the color deviations (ΔE) between the 

virtual model and reality for five buildings. The bars indicate the different architectural elements, with higher values 

representing significant color deviations. 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

116 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

 

Fig.6 - Histogram representing the Color Validity Index 

(CVI) of the analysed buildings in the city of Helsinki. The 

lower CVI value indicates higher color fidelity. 
 

7. Optimisation strategies 

Optimisation of areas with high ΔE. For areas with ΔE 

greater than 50 (e.g. cathedral roof, town hall door), 

optimisation of reflectance and material texture mapping is 

required to reduce color differences. 

 

Color Space Alignment. To precisely align RGB hues 

between the virtual and real models, color management 

software such as ColorMunki or Spyder can be used for 

consistent color calibration. 

Improved light simulation. The lighting parameters in the 

virtual model must be adjusted to reflect real conditions. 

The use of a full spectrum render can improve color fidelity 

compared to ambient lighting. 

Introduction of color correction algorithms. Algorithms 

such as gamma correction, color temperature adjustment 

and brightness balancing can automatically compensate 

for color differences. 

Real-time CVI Monitoring System. To maintain high color 

fidelity, a continuous CVI monitoring system is useful, 

which periodically detects and corrects discrepancies 

between the virtual model and reality. 

The implementation of these strategies can significantly 

improve the color validity of digital twins, ensuring a more 

accurate match and a better visual experience in the urban 

context.  

Figure 7 shows the example of the City Hall where after the 

application of the strategies, the CVI dropped to 4.82, an 

improvement of 92.83%, demonstrating a true color match. 

 

 

Fig.7 - The City Hall before and after optimisation 

 

8. Conclusions 

The present study explored color management in digital 

urban twins, focusing on the case of Helsinki. Through a 

comparison of the color data from the Digital Twin and 

those obtained from Google Earth, critical issues affecting 

the quality of the digital representation emerged, 

especially in terms of brightness and saturation. However, 

the investigation showed that effective color control is 

possible by using advanced color analysis software and 

calculating color differences (ΔE) with the Chromatic 

Validity Index (CVI). This approach made it possible to 



Color differences between Urban Digital Twin and the real city. Optimisations and compensations. 

117 Color Culture and Science Journal Vol. 17 (2)  DOI: 10.23738/CCSJ.170212 

accurately measure the color fidelity between the virtual 

model and the real scene and to propose targeted 

optimisation strategies.  

The results of this study support the use of Urban Digital 

Twins as valuable tools not only for color management, but 

also for urban design and redevelopment. With proper 

color control, Digital Twins can improve the documentation 

and preservation of the visual identity of cities. To further 

increase color fidelity, the integration of Level of Detail 

(LOD) is recommended, including accurate color data on 

building façades and roofs, obtained through field surveys 

with advanced digital technologies. 

Finally, future research could extend these strategies to 

other urban contexts, with the aim of confirming the validity 

of the results and promoting the adoption of digital twins 

as fundamental tools for integrated urban management, 

including color aspects for visually coherent and 

sustainable cities. 

 

9. Conflict of interest declaration  

The authors declare no conflict of interest including 

financial, personal or other relationship with other people 

and organizations within three years of beginning the 

submitted work that could inappropriately influence, or be 

perceived to influence, this work. 

 

10. Funding source declaration 

This research did not receive any specific grant from 

funding agencies in the public, commercial, or-not-for-

profit sectors. 

 

11. Acknowledgment  

This paper, with some changes in the text and in the 

figures, was published by the authors with the title: 

Martone M. and Fan, T. (2024) 'Un nuovo approccio 

alla gestione del colore negli Urban Digital Twins' in 

Cherubini;F. and Siniscalco A. (edited by) Colore e 

Colorimetria. Contributi Multidisciplinari. Milano: 

Gruppo del Colore - Associazione Italiana Colore, pp. 

96-103. ISBN 978-88-99513-26-9. 

The images in Figure 1 are taken from: 

https://www.vopoint.com/algorithms_14.html 

https://www3.weforum.org/docs/WEF_Digital_Twin_Cities

_2023.pdf (Accessed: 2 April 2025). 

Figures 2 to 7 are edited by Tiantian Fan 

 

12. Short biography of the authors 

Maria Martone - Architect. Associate Professor in 

Drawing, has taught at the Sapienza University of Rome. 

She carries out her scientific research on the themes of 

representation and critical documentation of architecture, 

the city and the territory with the application of digital 

techniques related to surveying, photomodelling and 

geographic information systems. The results of her 

research are published in numerous scientific journals, 

conference proceedings and monographs. 

Tiantian Fan - Ph.D. in Architecture; Lecturer, Wuhan 

Donghu College (Wuhan, China). Her research focuses on 

color design and urban/fashion applications, combining 

digital simulation, color management, and multisensory 

communication in cultural heritage and contemporary 

design. 

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