EXTRACTING DETAILED BUILDINGS 3D MODEL WITH USING …...INTRODUCTION 1.1. Extraction Digital Surface Model ... Food Science, Biotechnology and Water Resources Engineering. Some of

http://www.iaeme.com/IJCIET/index.asp 1097 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 7, July 2018, pp. 1097–1108, Article ID: IJCIET_09_07_115

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=7

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

EXTRACTING DETAILED BUILDINGS 3D

MODEL WITH USING HIGH RESOLUTION

SATELLITE IMAGERY BY REMOTE SENSING

AND GIS ANALYSIS; AL-QASIM GREEN

UNIVERSITY A CASE STUDY

Hayder Dibs

Department of Hydraulic structures Engineering,

Faculty of Water resources Engineering, Al-Qasim Green University, Iraq

Suhad AL-Hedny

Department of Environment, Faculty of Environmental Science,

Al-Qasim Green University, Babil, Iraq

Hasan Saad Abed Karkoosh

Department of Environment, Faculty of Environmental Science,

Al-Qasim Green University, Babil, Iraq

ABSTRACT

Three dimensions map presents the earth’s surface and gives better representing

compared to two dimensions map. However, using traditional approaches to create

digital surface model is not efficient for the need of earth features’ details, simply

because, it represents only three dimensional objects in one texture and does not offer

any realistic depiction to real world. Add to that the demand for up-to-date and

accurate geo-information covering urban area is rising considerably. Therefore,

getting digital surface models with highly details is challenging. This research thus

proposes a new technique to overcome this problem. The proposed technique involves

integrating of remote sensing, Geographic Information System with Architecture

software environment to generate three dimension model. Our method starts with,

applied high resolution image of WorldView-3 satellite, then image preprocessing and

processing; geometric correction, geo-referencing and radiometric correction. After

that generating a 2D map of interesting area, then we created digital surface model by

extrusion of outlines buildings height. Then, converting the generated digital surface

model to multi-patch layers. Finally, build a 3D model for each object separately.

That has given digital surface model more realistic and near to real world comparing

to that one generated digital surface model. Results show the obtained digital surface

model with highly details is more effectiveness to applying for a different applications

such as environmental studies, urban development and expansion planning, in

addition to the diverse shape understanding tasks.

Extracting Detailed Buildings 3D Model with using High Resolution Satellite Imagery by Remote

Sensing and GIS Analysis; Al-Qasim Green University a Case Study


Key words: Digital surface model, remote sensing, GIS, Three dimension model,

Google SketchUp.

Cite this Article: Hayder Dibs, Suhad AL-Hedny, Hasan Saad Abed Karkoosh,

Extracting Detailed Buildings 3D Model with using High Resolution Satellite Imagery

by Remote Sensing and GIS Analysis; Al-Qasim Green University a Case Study.

International Journal of Civil Engineering and Technology, 9(7), 2018, pp. 1097-

1108.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=7

1. INTRODUCTION

1.1. Extraction Digital Surface Model

Non-availability of geospatial dataset is one of the biggest problem to study any area. In

additional, digital elevation model (DEM) not enough to get overview of the study area in

details, and to see all of the features that locate in area of interest [1]. In other side, extraction

digital surface model (DSM) gives just sold objects and details of any objects in the area of

interest is not clear [2-3]. Remote sensing satellites produce high spatial, temporal and

spectral-resolution images such as the IKONOS and QuickBird, that lead to a new era in earth

monitoring and observation, also with different remote sensing applications have been begun

[4-5]. The possibility of the high resolution satellite sensors, such as IKONOS and QuickBird

to change their viewing angle in one orbit give them the capability to obtain stereo or even

triple-overlapped images to create DSM [6 - 11].

The integration of 3D objects into their environment is essential for development of

sustainable management system [12 - 16]. SketchUp software is another powerful software

enables user to create 3D models with a highly accuracy details, and the combination of GIS

functions with SketchUp software has brought forward as an easy to quick modelling, which

works well for creating 3D objects model [17]. In an attempt to improve data management,

[18] used GIS 3D spatial analysis to integrate spatial data of university campus, their study

showed the effectiveness of using 3D GIS model to support design and planning for specific

applications at regional level.

DSM generation requires many processing steps such as sensor operation, modeling,

stereo matching, editing and interpolation [19-22]. All these steps contribute to generate high

quality of DSM, among which stereo matching is crucial to the accuracy and completeness of

DSM [23-25]. The use of the automated DSM which generation from satellite images is still

being considered difficult, if not impossible [26]. It normally takes several hours of

computation to generate a DEM, besides the time required for operators’ measurements add to

that resulting DEMs often has unfortunately, have accuracy and completeness problems and

longer time is required to manually correct errors [27-30-31-32].

1.2. Extraction DSM with Computer Vision

Despite the fact that a map with two dimensions in x and y was adequate to chart buildings,

roads or any other features and the charted in 3 dimensions has become more popular in

different mapping related area [33-36].The growing and integrating different technologies has

facilitated and enhanced means of data for greater user-interaction such as remote sensing,

GIS and computer ability lead to a better management, visualization and developing of 3D

models [37- 43]. The main objective of this study is to create a robust 3D University campus

model by creating 2D map of the study area from digitizing the satellite imagery of study area

and use of the available of ancillary data of features located in the study area to establish

Hayder Dibs, Suhad AL-Hedny, Hasan Saad Abed Karkoosh


attribute tables and record features’ properties in Arc Map software, and then create DSM by

using the output of Arc Map to use in Arc Scene to generate the DSM of the study area. To

get DSM with more accurate, details, and close to real world, we attempted in this stage to use

the Google Sketchup software to generate 3D of the study area based on what we get in

second stage, by taking photos to all features (building, gardens, road,…, so on) locate in the

study area and use it in Google Sketchup software, which has been little used in previous

studies. Recommendations are made in the context of the generating and design 3D models

for surveying, remote sensing, GIS analysts and architectures, planning for the future

expansion projects, enable a variety of GIS spatial analysis and easy accessible to visitors,

students. Another purpose of this research is to study the effectiveness of using ArcGIS and

SketchUp models to create a site-linked 3D University campus.

2. METHODOLOGY OF CREATING 3D MODEL

The proposed method was used to get an accurate DSM then 3D model in details of the Al-

Qasim Green University campus (see Figure 1). Most of getting 3D model in geospatial field

approaches was done from applying different algorithms, approaches, techniques in Arc Map

and Arc Scene software to get the 3D model [25, 43]. However, this 3D model not enough to

give the analysts or researchers a good view about the study area. That because, all generate

DEMs, and DSMs are in solid texture and they are far away from reality. For that we propose

a new method to extract the 3D model with highly details as indicated in "Figure2".

Figure 1 Creating 3D model flow chart of methodology.

Our technique starts as follow; (1) after we got high resolution satellite imagery, we did

geometric and radiometric corrections to our image, (2) fieldwork was the second stage to

observe some ground points (GPs) around the university and to capture some photographs to

use in the next stage, (3) in this stage we used the GPs to perform geo-referencing to our

satellite imagery, (4) by using Arc Map 10.3v we create geodatabase and then we started to

conduct digitizing for all the study area to get a shapefile of the Al-Qasim Green University’

features, we also involve an ancillary data about the university campus such as: buildings

names and heights to create attribute of each features in the University, (5) the output




shapefile of Arc Map software is 2D map, we applied this file in Arc Scene platform and by

help of features attribute we generate the DSM of the university campus, (6) in this stage, we

looking for creation 3D model of each object locate in the study area by using the captured

photos of main campus and the generated DSM of the university by using the Google

SketchUp software. We did that by convert the DSM from file extension use under Arc Scene

to extension file work under Google SketchUp software to create 3D object texture.

Study Aerea

The study area is Al-Qasim Green University campus in Al Qasim city located in south of

Babil province, it is one of the provinces in central Iraq south of Baghdad, and the fifth largest

province in terms of population in Iraq. Its estimation of population about 2, 000 000 million.

The location of the Al-Qasim Green University in longitude and latitude of 44° 40ʹ 38.30ʺ E

and 32 ° 18ʹ 21.25ʺ N. University of Green is an Iraqi university specialized in teaching

materials related to engineering water resources, environmental, agricultural and veterinary

sciences with many other specialties. The university is a new one, it was established in 2012

after the disconnection of the Faculty of agriculture and veterinary medicine from the

University of Babylon and transferred to the new university followed by the opening of new

different faculties of Environmental Sciences, Food Science, Biotechnology and Water

Resources Engineering. Some of its faculties in Al-Qasim city, however, others distribute in

Hilla city the center of Babil province. The area of the study area (Al-Qasim Green

University) approximately is 2000m2, as shows in Figure2 below

Figure 2 The study area location in Al-Qasim city south of Babil state, Iraq

Figure 2 depicts the study area, on the left side of this figure the Iraq map and also it is

showed Babil province, and the Al-Qasim Green University locate in a Al-Qasim city in the

south of Babil province as show in the below right side of Figure2.

3. THE USED DATASET

Three types of dataset have been used to perform this research. They are: (1) The WorldView-

3 satellite image; (2) Ancillary data of the study area; (3) data collected from fieldwork. The

region has an area of 5 km2 (see Figure 3). The WorldView-3 satellite image has been used in

this research. The WorldView-3 satellite sensor was licensed by the National Oceanic and

Atmospheric Administration (NOAA) to collect in addition to the standard panchromatic and

multispectral bands, and eight-band short-wave infrared (SWIR) and 12 CAVIS imagery. The



WorldView-3 is the first multi-payload, super-spectral, high-resolution commercial satellite

sensor operating at an altitude of 617 km. WorldView-3 satellite provides 31 cm

panchromatic resolution, 1.24 m multispectral resolution, 3.7 m short wave infrared resolution

and 30 m CAVIS resolution. The satellite has an average revisit time of <1 day and is capable

of collecting up to 680,000 km2 per day.WorldView-3 satellite bears a strong resemblance to

WorldView-2 launched on October 8, 2009 in terms of its performance characteristics. The

WorldView-3 satellite sensor benefits from significant improvements including cost savings,

risk reduction, and faster delivery for its customers [44]. The image was captured over Al-

Qasim city, Babil province, Iraq on 15th October 2016, and it is corrected radiometriclly and

geometrically to reduce the noise in our satellite imagery. Figure 3 shows the WorldView-3

image and the study area.

Figure 3 WorldView-3 satellite image and study area.

Figure 3 shows Al-Qasim Green University, which is in the middle and upper part of the

imagery. Another type of dataset we got is an ancillary data, it is a hardcopy of data sheets

includes of different buildings details that locate in the university such as (names, heights,

areas, usages). The third type of dataset that we used for this research is collected by

fieldwork as we will explain in the following section.

4. THE FIELD WORK

Fieldwork includes two steps. The first step was collection of ground reference points, which

is an important step to perform the geometric correction for the next steps. These reference

points should collect from the study area [45]. Garmin 76CSX global positioning system was

used to collect the ground reference points of some features located in the study area. The

fieldwork done by using Handheld GPSMAP type Garmin Csx76. The Garmin GPSMAP

76CSx is the most popular GPS instruments that use for many applications in outdoor and

marine [47-52].The ground reference data were collected on July 15, 2017 of 7 ground

reference points surrounding the Al-Qasim Green University were recorded as shown in Table

1. On the other hand, the second steps in fieldwork was captured some photos of university

campus, to use in the next steps of create the 3D model of interesting area.




Table 1 The collected ground reference points.

No. Northing Easting

1 32 18 37.37 44 40 27.29

2 32 18 19.90 44 40 31.00

3 32 18 20.90 44 40 42.49

4 32 18 36.52 44 40 42.18

5 32 18 29.71 44 40 46.36

6 32 18 25.34 44 40 40.35

7 32 18 35.00 44 40 31.79

5. RESULTS AND DISCUSSION

Processing of this research conducted throughout three stages as mentioned in the section 2.

Firstly, the pre-processing step, in this stage we started with downloading the high satellite

resolution imagery of our study area (Al-Qasim Green University) from WorldView-3

satellite as mentioned in section 2.2, and the preprocessing of this step include performing the

radiometric and geometric corrections to reduce the noise that got during image capturing

[25]. Secondly, the processing. In this stage, we geo-referenced the WorldView-3 satellite

imagery through coordinates matching between the collected ground reference points from

fieldwork with their corresponding point locations that appear in satellite image (see Figure3),

we did this geo-referencing to give the image’ features the right position and shape of all

these features[21, 28, 54, 55]. Figure 3 shows the study area image after performing the

radiometric and geometric corrections and also geo-referencing. The digitizing of the satellite

image was the first step to get the footprint (2D map) of the study area. This step was a basic

input in Arc Map software to consider it in the next steps for generate DSM. For our

digitizing step, we generate fife layers, each layer carries different information to each other,

and they are (buildings, gardens, street, boundaries and trees) layers that locate in the

university campus. Figure4 indicates the digitizing satellite imagery and its generated layers

of university campus and the main building layer that colored with red color.

Figure 4 The digitized image and its layers of university campus.

The digitizing step, was used also to establish the geodatabase and attribute tables for each

digitized feature in the satellite imagery. The ancillary data helped to expansion these attribute

by adding a new field to feature attribute. The attribute tables were established for each single

layer, one of the interesting field of this layers is a height field, that because we will consider



these heights in this field to generate the university campus DSM and it will help us also in

creating 3D model in the next steps. The final output shapefile of the Al-Qasim Green

University campus is shown in Figure5.The results found that the traditional 2D map is flat

with only two dimensions (X and Y), poorly shape understanding, and less effective for

individual building data extraction, but still a base map for any future [28].

Thirdly, the post processing. Our concern in this research is to get 3D model in details for

our study area (university campus). So, the first step in this stage was to create DSM of the

study area using Arc Scene environment from applying the generated shapefile in previous

step. After that we arise the third dimension (height) based on using the features’ attribute

[23-24]. The created DSM has done by extrusion the height attribute of each features located

in the university campus under Arc Scene environment. Figure 6 shows the generated DSM of

the university campus.

Figure 5 The campus of Al-Qasim Green University shapefile

Figure 6 The DSM of Al-Qasim Green University buildings

The building colors are changed according to the differences in heights of building as

shown in Figure 10, which depicts this concept. After getting the DSM of Al-Qasim Green




University buildings, it is almost a three model in one solid and texture, there is no any real

representing for the University campus as indicated in Figure6. We need to present the

university campus and its faculties’ buildings in details and close to reality. Therefore, we

went through the second step in the post processing stage, it is the processing of convert the

Arc Scene extension file (DSM) to extension able to use under Google SketchUp environment

and create three dimensions model more realistic. So, we start by:

First, converting Arc Scene DSM to Multi-patch file with the “Layer to 3D Feature

Class”. Second, then we convert the Multi-patch to a collada file using the “Multi-patch to

collada” tool. From the experimental result we found that the converted file will has all the

attributes and characteristics of each feature individually except different in scale. Figure 7

show the converted main building in Google SketchUp environment with the satellite imagery

of the study area as shown. Third, then we modified the converted file to create an accurate

3D model for each feature located in the study area. Figure8 shows the 3D model of the study

area.

Figure 7 3D view of main building with the satellite imagery of the study area.

Figure 8 The final 3D model of the study area.

The experiment result shows that the final 3D model is more accurate and give more

details than the DSM [23] [24]. It is possible to use the final 3D model under Google



sketchUp and also with the Arc Scene environment by doing replacement in Arc Scene of the

Collada’s geometry file with the file that created by Google SketchUp[54]. Integrate GIS with

SketchUp, allows for a better utilization of all necessary geographic information for all

buildings and features located and existed inside the university campus, add to that the

established attribute provides the users what they want in case of any future work to develop

the study area [21, 28, 44, 57]. Moreover, this integration allowed to get a richer performance

of the campus model through smoothly model export and import among different software

[21, 28]. So, it is a key concept of facility management, effectively link the building model to

site model, providing realistic view for visitors, adaptation of in progress changing, and allow

to better visualize and analyze the data for existing buildings [58].

6. CONCLUSIONS

Obtaining DSM and/or DEM in one color and texture is not adequate to a demand of

geospatial needs nowadays. Therefore, this research presents a new method to overcome this

problem. Our technique involves three main steps; pre-processing, and it is started with input

the satellite imagery, and then correct it this image radiometrically and geometrically. After

that the processing step, in this step we use the ancillary and fieldwork datasets to help us to

do the geo-referencing to our image of the study area, the we did the geodatabase to start for

digitizing our image to get 2D map of the university campus. In all pre-processing and

processing steps we got DSM and we used different software ENVI, Arc Map and Arc Scene

environment. In addition, in (3) post processing step, we converted the extension file of our

DSM to another one able to use under the Google SketchUp environment to build our

3Dmodel based on the built DSM in previous steps. The obtained 3D model give the

researchers more details and close to real world. The final results of the 3D model shows

perfect 3d model of the study area and the effectiveness of getting 3D model compared to the

obtained DSM, also the highly details that we got from our 3D model, and can apply for a

different applications such as environmental studies, urban development and expansion

planning, and diverse shape understanding tasks.

ACKNOWLEDGMENT

We acknowledge all who support us to complete this work.

REFERENCES

[1] K. Di, R. Ma, and R. Li, "Automatic shore line extraction from high-resolution IKONOS

satellite imagery". Proc. of ASPRS 2003 Conference, Anchorage, Alaska, May 5-9.

[2] X. Hu, and C. V. Tao,"Automatic extraction of main-road centerlines from IKONOS and

QuickBird imagery using perceptual grouping" Proc. of ASPRS 2003 Conference,

Anchorage, Alaska, May 5-9.

[3] C.I. Chang, "Hyperspectral imaging: Techniques for spectral detection and classification"

New York: Plenum Publishing Co. 2003

[4] J. Poon, C. Fraser, C. Zhang, L. Zhang, A. Gruen, A. (2005). "Quality Assessment of

Digital Surface Models Generated from IKONOS Imagery". Photogrammetric Record, 20

(110), 2005, PP. 162-171.

[5] T. Krauss, P. Reinartz, M. Lehner, M. Schroeder, M. Stilla, "DEM generation from very

high resolution stereo satellite data in urban areas using dynamic programming". ISPRS

Hannover Workshop 2005 on “High-Resolution Earth Imaging for Geospatial

Information”, Hannover, Germany, 17-20, May.




[6] L. Zhang, and A. Gruen, "Multi-Image Matching for DSM Generation from IKONOS

Imagery". ISPRS Journal of Photogrammetry and Remote Sensing, 60 (3), 2006, pp. 195-

211.

[7] C. Zhang, E. P. Baltsavias, L. Sullivan, "Performance evaluation of ATOMI system for

road database updating from aerial film, ADS40, IKONOS and Quickbirdorthoimagery".

International Archives of Photogrammetry and Remote Sensing, 2005, 29-30 August,

Vienna, Austria.

[8] H. Sohn, C. Park, H. Chang, "Rational function model based image matching for digital

elevation models". Photogrammetric Record, 20 (112), 2005, PP. 366-383.

[9] [9] W. Greenberg, J. Lomax, J. Peteet, M. Torres, & V. Cowell, "Religious/spiritual

commitments and psychiatric practice"Resource document. American Psychiatric

Association, 2006.

[10] J. Poon, C. Fraser, C. Zhang, "Digital surface models from high resolution satellite

imagery". Photogrammetric Engineering and Remote Sensing, In press, 2007.

[11] S. Rich, and K. H. Davis, "Geographic Information System (GIS) for facility

Management". IFMA, 2010.

[12] S. M. Meier, "The mapping of Saint Mary's University of Minnesota Campus using a

Geographic Information System (GIS)". Department of Resource Analysis, Saint Mary's

University of Minnesota, Winona, 2010.

[13] I. Hijazi, M. Ehlers, & S. Zlatanova, "BIM for Geo-Analysis: Set Up of 3D Information

System With Open Source Software and Open Specification". 5th Internation 3D

GeoInfoconference.Berlin, German, 2010.

[14] L. Liu, "what is GIS". Retrieved from Gisforum: 2011,

http://www.gisforum.net/show.aspx?id=301&cid=14

[15] C. Dempsey, "What is GIS?" Retrieved July 1, 2011, from GIS Lounge:

http://www.gislounge.com/what-is-gis/ ,45, 6, 2017, PP. 927–937,

[16] M. T. Eismann, "Hyperspectral remote sensing". Bellingham: SPIE. 2012

[17] Y. Mu, and H. Gao. H. "Research on GIS 3D Visualization based on ArcGIS and Sketch

up". book series (LNEE, volume 216, (69), 2012.

[18] A. Al-Rawabdeh, N. Al-Ansari, H. Attya, S. Knutsson, "GIS Applications for Building 3D

Campus, Utilities and Implementation Mapping Aspects for University Planning

Purposes" Journal of Civil Engineering and Architecture, ISSN, 2014, pp. 1934-7359,

USA.

[19] T.G. Farr, P. A. Rosen, E. Caro, R. Crippen, R. Duren, "Shuttle Radar Topography

Mission. Reviews of Geophysics" , 45 RG2004, Paper number 2005RG000183, 2007, pp.

1-33.

[20] C.C. Chang, & C.J. Lin, "LIBSVM: A library for Support Vector Machines". ACM

Transactions on Intelligent Systems and Technology (TIST), 2(3), 27, 2011.

[21] J. A. Slater, B. Heady, G. Kroenung W. Curtis, J. Haase "Global assessment of the new

ASTER global digital elevation model". Photog Eng and Remote Sensing , 77, 2011, PP.

335-349.

[22] J. Jiang, D. Michael, R. He, Q. Cai, Y. Chen, & J. Xu, "Judgment of CO2 leaking in

underground storage using spectral characteristics of soybean". Transactions of the

Chinese Society of Agricultural Engineering, 29(12), 2013, PP. 163–169.

[23] N. Haala, H. Hastedt, K. Wolf, C. Ressl, S. Baltrusch, S. "Digital photogrammetric

camera evaluation generation of digital elevation models". Photogramm. Fernerkund.

Geoinf.



[24] T. Kim, S. Rhee," DEM Generation from High Resolution Satellite Images through a New

3D Least Squares Matching Algorithm". In Proceedings of the ISPRS Hannover

Workshop 2011 “High-Resolution Earth Imaging for Geospatial Information”, Hannover,

Germany, 14–17 June

[25] D. Hayder, M. Shattri, A. Noordin, A. & P. Biswajeet, (2015). "Band-to-band registration

model for near-equatorial Earth observation satellite images with the use of automatic

control point extraction", International Journal of Remote Sensing, Vol. 36, No. 8, 2015,

PP. 2184–2200. http://dx.doi.org/10.1080/01431161.2015.1034891

[26] [26] N. A. Hameid, A. Bannari, and G. Kadhem, "Absolute Surface Elevations Accuracies

Assessment of Different DEMs Using Ground Truth Data Over Kingdom of Bahrain"

Journal of Remote Sensing & GIS, 2016.

[27] A. Davood, "Improving Spectral–spatial Classification of Hyperspectral Imagery Using

Spectral Dimensionality Reduction Based on Weighted Genetic Algorithm". Journal of the

Indian Society of Remote Sensing.

[28] D. Hayder, I. Mohammed, B. Goma, B. and M. Shattri, "Hierarchical classification

approach for mapping rubber tree growth using per-pixel and object-oriented classifiers

with SPOT-5 imagery", The Egyptian Journal of Remote Sensing and Space Sciences,

2017, Production and hosting by Elsevier B.V.

[29] B. Kousik, C. Debashish, M. Pabitra, M. Arundhati, " Spatial-Correlation Based

Persistent Scatterer Interferometric Study for Ground Deformation". Journal of the Indian

Society of Remote Sensing. 45, 6, 2017, PP. 913–926

[30] Kadhim Naief Kadhim and Ghufran A.( The Geotechnical Maps For Gypsum By Using

Gis For Najaf City (Najaf - Iraq). (IJCIET), Volume 7, Issue 4, July-August 2016, pp.

329–338

[31] Kadhim Naief Kadhim 'Estimating Of Consumptive Use Of Water In Babylon

Governorate-Iraq By Using Different Methods' (IJCIET)Volume 9, Issue 2, February

2018, pp. 798–807

[32] K. Venkateswaran, N. Kasthuri, & N. Kousika, "Performance Comparison of

Multiwavelet and Multicontourlet Frame Based Features for Improving Classification

Accuracy in Remote Sensing Images". Journal of the Indian Society of Remote Sensing,

Volume 45, Issue 6, 2017, pp. 903–911.

[33] Y. Li, H. He, L. Han, J. Yang, H. Bo, "Design and implementation of virtue campus of

Xi’an Jiaotong University", Exp. Technol. Manag. 2 (5), 2001, PP. 38-45.

[34] R. Nagi, "Cartographic visualization for mobile applications. Enschede" , 2004: ITC

Library.

[35] M. Kraak, "Why maps matter in GIScience" Cartographic Journal, 2006, PP. 82-89.

[36] D. Hayder, M. Shattri, A. Noordin P. Biswajeet, "Registration model for near-equatorial

earth observation satellite images using automatic extraction of control points" , ISG

conference, 2014

[37] A. M. Shehata, and N. A. Koshak, "Using 3D GIS to Assess Environmental Hazards in

Built Environmental", Journal of Al Alzhar University, Engineering Sector, Cairo, Egypt,

2006.

[38] V.K. Bansal, "Potential of GIS to Find Solutions to Space Related Problems in

Construction Industry" World Academy of Science, Engineering and Technology, 32,

2007.

[39] G. A. N. Andrienko, "Spatio-temporal Aggregation for Visual Analysis of Movements"

IEEE Symposium on Visual Analytics Science and Technology. 2008.

[40] S. K. Spirou, C. Ioannidis, & C. Potsiou, "Proposal for the Development of a 3D hybrid

model for the Hellenic Cadastre". 2nd International Workshop on 3D Cadastres, 2011, (p.

36).




[41] C. Kalil, and G. Braswell, "Auto Map 3D and Autodesk Map Guide Enterprise":

Powerful, Affordable and Open GIS, 2008, USA.

[42] Bentley System. (n.d.). Bentley PowerCivil V8i. Retrieved July 10, (2011) from Bentley:

http://www.bentley.com/en US/Products/Bentley+PowerCivil/Product-Overview.htm

[43] M. Wang, "Building information Modeling (BIM): Site-Building Interoperability

Methods". Master’s thesis, Worcester Polytechnic Institute, Interdisciplinary Construction

Project Management, 2011.

[44] D. Hayder, I. Mohammed, S. Vaheda, S, and M. Shattri,"Automatic Keypoints Extraction

from UAV Image with Refine and Improved Scale Invariant Features Transform (RI-

SIFT)", International Journal of Geoinformatics, Vol. 12, No.3, 2016.

[45] R.J. John, (2005)" introduction to Digital Image processing, A Remote sensing

Perspective", 3, 2005, p.239-247. Pearson prentice hall.

[46] R. J. John, R.J. (2007). "Remote sensing of the Environment", An earth resource

Perspective, 1, PP. 148-189.Pearson prentice hall.

[47] J.R. Anderson, E.E. Hardy, J.T. Roach, R.E. Witmer, "A Land Use and Land Cover

Classification System for Use with Remote Sensor Data" Government Printing Office:

Washington, DC, USA, 1976.

[48] B. Zhao, B. He, & Y. Cong, "Destriping method using lifting wavelet transform of remote

sensing image". International conference on computer, mechatronics, control and

electronic engineering CMCE, 2010, PP. 110–113.

[49] X. Zhenliang, S. Z. Yi, Y. L. Ma, "Image Pose Estimation Based on Procrustes Theory".

Journal of the Indian Society of Remote Sensing. 45, 6, 2017, PP. 939–943.

[50] Zhe, and J. M. Fox, "Mapping Rubber Tree Growth in Mainland Southeast Asia Using

Time-Series MODIS 250 m NDVI and Statistical Data". Applied Geography 32(2), 2011,

PP. 420-432.

[51] A. Hayder, "Feature Extraction and Based Pixel Classification for Estimation the Land

Cover thematic Map using hyperspectral data", International Journal of Engineering

Research and Applications, 3, (3), 2013.

[52] Q. Xiaojun, H. L. Deshuai, Yuan, "Urban Shadow Detection and Classification Using

Hyperspectral Image". Journal of the Indian Society of Remote Sensing. 45, 6, 2017, PP.

945–952.

[53] D. Hayder, M. Shattri, A. Noordin P. Biswajeet, "Band-to-band registration model for

near-equatorial Earth observation satellite images with the use of automatic control point

extraction", 2015, International Journal of Remote Sensing.

[54] J.B. Campbell, & R.H. Wynne, Introduction to remote sensing. New York City: Guilford

Press, 2011.

[55] C. Alireza, A. Rahim, A. Abbaspour, & Y. Dell, "An Improvement on the Clustering of

High-Resolution Satellite Images Using a Hybrid Algorithm" Journal of the Indian

Society of Remote Sensing, 45, 4, 2017, pp. 579–590

[56] G. Hake, D. Grünreich, and L. Meng, "Kartographie - Visualisierungraum-zeitlicher

Information". Berlin - new York, 2002.

[57] C. Häberling, "Cartographic design principles for 3D maps- A contribution to cartographic

theory" . In: Proceedings of 22nd ICA International Cartographic Conference, ACoruńa,

Spain, 2005, p. 99–115.

[58] J. Woo, J. Wilsmann, & D. Kang, "Use of As-Built Information Modeling", Innovation

for Reshaping Construction Practice, 2011.

Documents

EXTRACTING DETAILED BUILDINGS 3D MODEL WITH USING …...INTRODUCTION 1.1. Extraction Digital Surface Model ... Food Science, Biotechnology and Water Resources Engineering. Some of