Preliminary Result of 3D City Modelling For Hanoi, Vietnam44971

In order to develop 3D city models, 2D geographic data such as building footprint and building height attribute are required.. Keywords— Hanoi, 3D city models, building heights, buildi

Preliminary Result of 3D City Modelling For Hanoi,

Vietnam

Phan Anh

Center of Multidisciplinary Integrated

Technology for Field Monitoring

University of Engineering and

Technology, VNUH

Hanoi, Vietnam

anhp@fimo.edu.vn

Nguyen Thi Nhat Thanh

Center of Multidisciplinary Integrated

Technology for Field Monitoring

University of Engineering and

Technology, VNUH

Hanoi, Vietnam

thanhntn@fimo.edu.vn

Chu Thua Vu

Center of Multidisciplinary Integrated Technology for Field Monitoring University of Engineering and Technology, VNUH

Hanoi, Vietnam vuct@fimo.edu.vn Nguyen Viet Ha

University of Engineering and Technology, VNUH

Hanoi, Vietnam hanv@vnu.edu.vn

Bui Quang Hung

Center of Multidisciplinary Integrated Technology for Field Monitoring University of Engineering and Technology, VNUH

Hanoi, Vietnam hungbq@fimo.edu.vn

Abstract ² Hanoi is the one of the fastest-growing cities in

Vietnam, which sets the target to turn into a smart city in 2030

Nowadays, 3D city models are being increasingly employed for

many domains and tasks beyond visualization, then it will take an

important role in smart city In order to develop 3D city models,

2D geographic data such as building footprint and building height

attribute are required However, the lack of the height attribute for

various types of building and low performance of rendering and

visualizing 3D city models are two big remaining problems In this

paper, available data from open sources is used to predict the

building height The prediction has carried out with machine

learning techniques using the combination of different attributes

After that, the models will be created using 3D tiles specification

to improve the visualization performance The preliminary results

of the proposed method highlight the potential of generation of

massive 3D city models from the available data in Vietnam

Keywords— Hanoi, 3D city models, building heights, building

footprint, 3D tiles

I. INTRODUCTION AND RELATED WORKS

Using digital maps to describe the real world is no longer

something new to us as we are moving into the 4th industrial

revolution 2D maps now cannot meet the growing demand of

people in urban management issues but a 3D map is a perfect

replacement for that By representing objects in 3D

environment, 3D maps always create compelling visibility

into all relevant areas, especially in the Geographic

Information System (GIS) GIS is a virtual geographic

environment, including systems of hardware, software,

geographic and human data, which are designed to capture,

store, update, control, and analyze all types of information

related to geographic information Through GIS, researchers

can easily look at phenomena, geographic-related changes in

the most general way, thus making the most accurate decisions

and solutions to the reality situation 3D GIS technology can

be considered as a virtual environment system which is full of

information about all objects in real life This will bring

benefits not only as a geographic information system but also

as a basis for a smart city system in the future

Currently, the use of 3D GIS technology to build virtual

city has been realized in many cities in the world, such as New

York [1], Berlin [2], etc These studies use 3D mapping

technology to display 3D object models with information such

as name, coordinates, elevation, etc of the object The 3D city maps of Berlin and New York City have hierarchical data display mechanisms based on user location and perspective The objects will not be displayed on the invisible part of the map This not only increases the accuracy of the user experiment but also reduces the amount of graphics processing and speeds up the system for a better experience

In particular, more than 1.1 million buildings without texture

in New York City and many textured buildings in scale 250 square kilometers in Berlin have been visualized in both systems, respectively

In Vietnam, the problem of building virtual city is still quite new and no such a 3D virtual city is reported Available systems such as OpenStreetMap, Google Earth have also been providing 3D models, but the number of these models is limited and most of them are skyscrapers In 2017, Phan Anh

et al developed The VNU Virtual Campus system which demonstrates the model of buildings and trees distribution in the campus of Hanoi National University [3] This system allows user movement as well as changing 360 degree viewing angles However, the system has many difficulties in loading and processing of large data In particular, manual data collection has been a major constraint to future large-scale system development The lack of cadaster data and difficulties in automatic processing data such as footprint detection, building height detection, etc are becoming the most challenges needed to be addressed in Vietnam In addition, in 2011, Dinh Thi Bao Hoa et al [4] have also built 3D models for the West Lake area to promote tourism The advantage of this system is ability of displaying 3D models of houses, trees, traffic with high accuracy However, there still has some drawbacks such as: the system is only visible on specialized software, it has no online capability, and is not scalable due to the use of existing manual 3D models Through these two products, the main problem for 3D GIS system development for Vietnam is data deprivation and data processing efficiency for a large amount of 3D models The most important thing to build virtual city is to have enough input data with high accuracy Depending on the level

of detail, data will contain the information of the building footprint, building height, story, roof type, roof height, etc In the previous works, the information is provided through a variety of sources with different quality for New York and

Berlin However, acquiring this information in Vietnam is

very difficult There is not such a complete set of free and

public data, especially building height Moreover, Vietnamese

houses have complex architectures and building footprints

often have complex shapes Currently, LiDAR,

high-resolution satellite imagery and aerial images are used largely

to gather information for 3D city modelling However, all of

these methods require costly equipment and many related

licenses

In order to deal with the problem of building height

deprivation, F Biljecki et al proposed a method for building

height estimation based on related data [5] In the study, a

prediction has been carried out with a machine learning

technique (random forest) using 10 different attributes and

their combinations which are footprints and attributes

available in volunteered geoinformation and cadaster Results

obtained in the study achieved a mean error of 0.8 m

Recognizing the feasibility of this approach, we will apply the

based method to deploy a machine learning model for building

height prediction using information that can be collected with

the capability and scale of Hanoi This will help to address the

lack of building heights in Vietnam These data will then be

used to build 3D models using open specification (3D tiles)

provide by Cesium to enhance the performance of rendering

and visualizing 3D models

II.STUDY AREA AND METHODOLOGY

A  Study area

Hanoi is the capital of Vietnam and the country's second

largest city by population The population in 2015 was

estimated at 7.7 million people Hanoi covers an urban area of

319.56 km2. Hence this area is a good option for our case study

(Fig 1)

Fig 1. Location of Hanoi in Vietnam

B  Data

To build 3D models of buildings in Hanoi, two

indispensable information are buildings heights and buildings

footprints While building footprints can be obtained through

open data sources on the Internet, building heights is failed to

gather because of non-existing sources Therefore, building

heights need to be collected in Hanoi by field

observations/measurements to develop estimation models

For the Hanoi area, through the consideration of the feasibility

of gathering attributes, we collected 194 building’s

information for training and testing the predictor Each

building will be described by four attributes as stated in Table

I They are inherited from the study of F Biljecki et al 2017,

in which features including purpose of use, perimeter, the area

of the building, the relationship between the perimeter and the area of the buildings are collected

TABLE I  ATTRIBUTES USED TO THE DEPLOY THE

PREDICTOR MODEL

No Attribute Description

4 NPI Normalized perimeter index NPI = ଶξగ஺

௉

C  Prediction methodology

The machine learning method that we use to build the model for predicting height is the Random Forest Regression This is a machine learning method built on the basis of a decision tree This method is used mainly for classifying, regression calculations It will make a lot of decision trees during training and then return the values as classes for the purpose of classifying or returning the predicted values of individual trees for the purpose of computation In fact, the use of the Random Forest in most cases is better than some of the other popular machine learning methods, such as decision trees, k-nearest neighbors, etc The Random Forest method can also work well with inadequate data and avoid the overlapping of the overflow model

To apply Random Forest into predictor model, Scikit-learn library was implemented using the Python programming language After the model was deployed, the results will be evaluated in terms of Mean Absolute Error – MAE (1), Relative Error – RE (3), Root Mean Squared Error – RMSE (3)

Mean absolute error or MAE is a measure of the difference between two consecutive variables The MAE unit is the unit

of the variables of the observed object Formula for calculating MAE:

ܯܣܧ ൌσ௡௜ୀଵȁݕ݊௜െ ݔ௜ȁ (1) Where yi is the predicted height value, and xi is the actual height of ith measurement, n is the number of test values

RE is used to measure the degree of relative error of the estimated value with a real value in the same observation object This measure is expressed in terms of percentages and

no units The formula for RE is as follows:

ܴܧ ൌͳ෍ȁݕ௜െ ݔݔ ௜ȁ

௜

௡

௜ୀଵ

With yi as the predicted value of the object, xi is the real value in the same prediction, n is the predicted number, i = 0,

1, 2, , n

RMSE is a frequently used measure of the differences between values predicted by a model or an estimator and the values observed The formula of RMSE is as follows:

ܴܯܵܧ ൌ ඨσ ሺݕ௡௜ୀଵ ௜݊Ԣ െ ݕ௜ሻଶ

(3)

Where yi is the predicted height value, and yi is the actual

height of ith measurement, n is the number of test values

D  CityGML

CityGML is an open data model standard with

XML-based format for the storage and exchange of virtual 3D city

models, issued by Open Geospatial Consortium (OGC) and

the ISO TC211 The aim of the development of CityGML is

to reach a common definition of the basic entities, attributes,

and relations of a 3D city model This is especially important

with respect to the cost-effective sustainable maintenance of

3D city models, allowing the reuse of the same data in

different application fields CityGML defines ways to

describe most of the common 3D features and objects found

in cities as 13 thematic classes (such as buildings, roads,

rivers, bridges, vegetation and city furniture) and the

relationships between them It also defines different standard

levels of detail (LoDs) for the 3D objects, which allows the

representation of objects for different applications and

purposes, such as simulations, urban data mining, facility

management, and thematic inquiries Data standardizing give

us the effective way to exchange and extent the scale of 3D

Geographic Information System.[6]

E  Cesium platform

Cesium –an open-source JavaScript library for the

world-class 3D globes and maps -raised by Cesium Consortium

community Cesium provides powerful functions such as

Virtual Globe, libraries for visualization image layers, 3D

models Especially, Cesium provides high-fidelity

time-dynamic simulation and 4-D visualization It can be run on

different browsers such as Google Chrome, Firefox

We used Cesium open specification – 3D tiles and Cesium

platform for high-performance of visualization

III. EXPERIMENT AND RESULTS

Experiments will be following up with these steps as

follows: (i) collecting input data, (ii) building a model to

predict building height, (iii) constructing a 3D model of

buildings with LoD1 details, and (iv) developing a 3D GIS on

Cesium platform

A  Collecting input data

The input data to build the system should be divided into

the following main types of data:

%XLOGLQJ IRRWSULQW: Building footprint was downloaded

via the API of the website: wikimapia.org (see Fig 3, Fig 4)

The data cover an area of 8423 buildings with 14 types of

buildings classified according to their intended use (Table II)

%XLOGLQJKHLJKWDQGIRXUIHDWXUHVwere collected from

field surveys 30 random points represent for 30 buildings are

selected for each type of house in 14 classes (Table II)

However, after removing points represented for wrong

building polygons, the data was left with 186 points and

divided into 5 regions as shown in Fig.5 For each building,

four feature including usage, area, perimeter, and Normalized

perimeter index are collected and calculate The number of building for each type is listed in Table II

Fig 2.Illustration of buildings footprints in Hanoi The data need to be pre-processed to remove the wrong buildings footprints These wrong polygons will cause the high error in the predicting process

Fig 3.Illustration of building footprints in Hanoi (imagery maps)

Fig 4.Illustration of building footprints in Hanoi (cadastre maps)

Fig 5.Field trip locations We collected data from 5

residential areas which are the crowed population

TABLE II  14 CLASSES OF BUILDINGS IN HANOI

EQUIVALENT TO ITS ATTRIBUTE VALUE.THIS WILL BE USED

TO DEPLOY THE BUILDING HEIGHT PREDICTOR

Attribute

Number of sampling points

building

8

2 Bar 5

3 Building 32

4 Cafe 11

5 Church 5

6 Hospital 12

7 Hotel 12

8 House 23

9 Museum 5

10 Office 12

11 Restaurant 11

12 School 10

13 Store 21

14 University 19

B  Deploying the height predictor model

In this section, we will present a machine learning model

to predict the building height using the Random Forest method

based on the data collected in the field trip as discussed in

section A

The data used to train the model will have four attributes

(see Tabel I) Where the attribute "Usage" will be positive

integers from 1 to 14 corresponding to the classification of the

building during fieldtrip, while remaining attributes which are

"Area", "Perimeter" and "NPI" will be real numbers The

results of the predictor model will be tested and evaluated

through the MAE (1), RE (2), and RMSE (3) parameters The

usage of building is a categorical feature so we first use

one-hot encoding to turn this into binary vectors to have a better

job in prediction

In order to make the model more accurate, we had to adjust

the parameters for the model using cross-validation and

grid-search technique The predictor model will use the training

of the building with the building footprints collected from the open sources After the training and predicting, the results is shown in “Fig 9 and Fig 10”

Overall accuracy are considered using three measures MAE, and RMSE, and RE (see Table III) It is acceptable for 3D building with the height error less than or equal 5 meters The error in each class is different and it is shown in Table IV TABLE III  OVERALL ERROR OF THE PREDICTOR

TABLE IV  EVALUATE THE ERROR OF THE PREDICTOR WITH

EACH CLASS

Class

(%)

Apartment building

Table IV shows that building with large footprint such as class Building or Church classes often cause high errors Large buildings are often very tall and with some cases like Building and Church, which are large but not too high, can cause very high error

C  3D modeling

The predicted heights will then be merged with footprints for the next step 3D modeling 2D cascade building footprint will be extruded to obtain 3D building models according to 3D Tiles specification 3D Tiles enable adaptive spatial subdivision in 3D, including k-d trees, quadtrees, octrees, grids, and other spatial data structures (Fig 6) Thus, it will reduce the cost of rendering each model based on the distribution of models and result in a balanced data structure 3D Tiles is also more flexible when the user zoom in or zoom out, the visible map tiles will replace with new higher-resolution map or lower-higher-resolution map, respectively

The building will be modeled LoD1 according to the

CityGML specification [7] which means the building will be

represented as a simple block This approach will not aim to

create 3D models with high level of details due to the error in

the predicting process To create 3D building in LoD1, the

footprint will be extruded to its predicted height The whole

3D modeling is processed using FME which is a powerful

software for geo-spatial data processing and transformation

Fig 6. An adaptive quadtree-like subdivision based on the

distribution of buildings

The results of generating 3D buildings models for Hanoi

is shown in Fig 7 With the rendering mechanism of 3D Tiles,

8423 buildings have been visualized with LoD1 according to

CityGML standard

Fig 7. Illustration of 3D buildings in Hanoi

Most of the buildings in the city are apartments and offices

or shops according to the pie chart as in Fig 8 This may

indicate that modern buildings are being built in Hanoi tends

to increase, recently

Fig 8. Illustration of proportion of building usage in Hanoi From the chart in Fig 9, the “Building” is the highest and most distributed building type in the city of Hanoi with the average height is around 40m to 45m In addition, the classes

of Restaurant, Food stores, Bar, House, and Cafe have the lowest height in the city with the average height is around 13m

Fig 9. Building heights distributions according to 14 classes

of building in Hanoi Fig 10 shows the average building height in each district

of Hanoi High buildings are mainly located in the suburbs (Ha Dong Thanh Tri, Hoang Mai, Thanh Xuan, Tu Liem) as recently new apartment buildings and new urban areas have grown rapidly in these areas The buildings in the inner city have a lower average height, however, there are some tall skyscrapers, but the residential areas in the inner city are predominantly indigenous They mostly live in the same house through different generations so the height of these houses is not too high (around 10-13 meters high) while the houses in old quarter which are very famous for traveling are only around 6 – 8 meters high

With the access to these kinds of information and the visualization of 3D building models, decision makers or real estate investors can be able to predict and do the price estimation

Fig 10.Average building height in each district

IV. CONCLUSION

In this study, we have addressed two main issues

remaining in limitation of 3D GIS in Vietnam which are the

lack of 2D cadaster data especially building height data, and

the performance of rendering 3D objects at a large scale With

the cadastral data in 2D containing the footprint information

including geometric and building information attributes, we

have applied machine learning method to deploy a predictor

model to gather the height which is an essential information

for 3D city modeling at LoD1 After that, a virtual city for

Hanoi had built using 3D Tiles to faster rendering process

However, there are some limitation remaining It is noticed

errors because of limited number of building samples and

those feature leads to high prediction errors In the future, we

plan to collected more samples and features of buildings using

LiDAR, high-resolution satellite, and aerial images to

improve the prediction quality

ACKNOWLEDGMENT This work has been supported by Vietnam National University, Hanoi (VNU), under Project No QG.18.36

REFERENCES [1] D J B Bloomberg, Michael R, “BIM Gidelines,”

New York City, Dep Des Constr., no July, pp 1–57,

2012

[2] M Kada, “The 3D Berlin Project,” Photogramm week, pp 331–340, 2009

[3] A P, “Development of virtual campus using GIS data and 3D GIS technology: A case study for Development of Virtual Campus Using 3D GIS Technology: a case study for Vietnam National University , Hanoi,” 2017, no December

[4] H D, “A 3d gis to design tour for tourists in west lake and surrounding area , hanoi capital , vietnam,” pp 3–11, 2011

[5] F Biljecki, H Ledoux, and J Stoter, “Computers , Environment and Urban Systems Generating 3D city

models without elevation data,” Comput Environ Urban Syst., vol 64, pp 1–18, 2017

[6] E Standard, T H Kolbe, C Nagel, and E Standard,

“Open Geospatial Consortium OGC City Geography Markup Language ( CityGML ) En- coding Standard,” 2012

[7] F Biljecki, “Level of detail in 3D city models,” 2017