i

HIGH RESOLUTION DIGITAL ELEVATION MODEL GENERATION BY SEMI

GLOBAL MATCHING APPROACH

SITI MUNIRAH BINTI SHAFFIE

A project submitted in fulfilment of the

requirements for the award of the degree of

Master of Science (Geomatic Engineering)

Faculty of Geoinformatic and Real Estate

Universiti Teknologi Malaysia

JUNE 2015

iv

This thesis dedicated to my parents,

Shaffie bin Man & Kalsom Binti Ahmad.

My siblings Mohd Rizuan bin Shaffie, Mohamad Naziman bin Shaffie

Mohamad Khoshi bin Shaffie and Siti Hajar bin Shaffie

My sister in law Nor Azlina Harun

My nephews Mohd Aqil Amsyal and Mohd Aqil Ramadhan

v

ACKNOWLEDGEMENT

Alhamdulillah, first and foremost, all praise to Allah, the Almighty, and the

Benevolent for His blessings and guidance for giving us the inspiration to embark on

this Master Project and instilling in all of us the strength to accomplish the report of

the project.

First of all, I would like to express special thanks to Dr. Khairulnizam bin M.

Idris for his roles, coordinator and supervisor of this Master Project, comments,

guidance, advice, monitoring and cooperation in the project performance and

preparation of this report.

Besides that, I was also grateful to my family, especially, to my parent and

siblings, friends and colleagues for their patience and cooperation during the project

undertakings and entire report making process. Special gratitude to my company,

Antaragrafik Systems Sdn. Bhd, and partner MK. Survey Sdn. Bhd and also

Topographic Section, Department of Survey and Mapping Malaysia (DSMM), on

their help and responsible in providing the data to achieve the objectives of the study.

My research would not have been possible without their helps.

Finally, I was also greatly indebted to the Faculty of Geoinformation and Real

Estate staffs, our lecturers for their support and painstaking care they have supported

in term of the ideas and materials for this Master Project.

Thank you.

vi

ABSTRACT

The photogrammetric point cloud is a revolutionary product that represents an

important breakthrough in Photogrammetry. The purpose of this research is to

establish the workflow in the generation of Digital Elevation Model (DEM) from the

photogrammetric point cloud. The photogrammetric datasets were acquired using

Digital Modular Camera 01 (DMC 01) over the Putrajaya area. The main steps for

Digital Elevation Model generation using the digital photogrammetric software were

involved post processing image and Global Positioning System (GPS) and inertial

measurement unit (IMU), aerial triangulation, and photogrammetric point cloud

generation, point cloud editing and surfacing. The whole process was successfully

performed and the output was delivered in the raster DEM format. The generated

Digital Elevation Model were statistically compared with DEM’s available from

airborne Light, Detect and Ranging (LiDAR) technique and ground control points

(GCP’s) to measure the accuracy of DEM produced by Semi Global Matching(SGM)

using Root Mean Square Error (RMSE) and F-test analysis. Based on the analysis,

horizontal accuracy meets the expected accuracy; 0.5-1.0 Ground Sampling Distance

(GSD) of images and sub-meter accuracy were obtained for vertical accuracy. In

closing, elevation data generated from the SGM approach with this accuracy meet the

standard practice of Department Survey and Mapping Malaysia (DSMM) for vertical

direction (height) requirement.

Key words: DEM, DMC01, GPS, IMU, LiDAR, GCP’s, RMSE, GSD

vii

ABSTRAK

Awanan titik fotogrametri adalah produk revolusi yang melambangkan satu kejayaan

penting dalam bidang Fotogrametri. Tujuan kajian ini adalah untuk mengkaji aliran

kerja di dalam penjanaan model ketinggian digital daripada awanan titik-titik

Fotogrametri. Data-data Fotogrametri telah dicerap dengan menggunakan kamera

DMC01 dan Putrajaya telah dipilih sebagai kawasan kajian. Langkah-langkah utama

yang terlibat semasa proses penghasilan model ketinggian digital dengan

menggunakan perisian digital Fotogrametri adalah seperti berikut; pasca-

pemprosesan imej dan GPS/IMU (pembetulan kedudukan dan orientasi),

penyegitigaan udara, penjanaan awanan titik-titik fotogrametri, penyuntingan awanan

titik-titik dan penghasilan permukaan model ketinggian digital Keseluruhan proses

telah berjaya dilaksanakan dan hasil disimpan di dalam format raster model

ketinggian digital. Model ketinggian digital yang telah dijana secara statistiknya

dibandingkan dengan model ketinggian digital yang lain dimana ia diperolehi

daripada kaedah bawaan udara LiDAR dan cerapan titik kawalan bumi bagi

mengukur ketepatan model ketinggian digital yang dihasilkan daripada teknik

Pemadanan Separa Global dengan menggunakan kaedah analisis RMSE dan Ujian-F.

Berdasarkan analisis, ketepatan mendatar memenuhi jangkaan ketepatan yang

diperlukan iaitu 0.5-1.0 resolusi imej dan ketepatan sub-meter telah diperolehi untuk

ketepatan menegak. Sebagai penutup, ketinggian data yang dijana melalui

pendekatan Pemadanan Separa Global dengan ketepatan yang diperolehi ini

memenuhi amalan standard Jabatan Ukur dan Pemetaan Malaysia untuk keperluan

arah menegak (ketinggian).

viii

TABLE OF CONTENTS

CHAPTER TITLE PAGES

DECLARATION ii

DEDICATION iv

ACKNOWLEDGEMENT v

ABSTRACT vi

ABSTRAK vii

TABLE OF CONTENTS viii

LIST OF TABLES xii

LIST OF FIGURES xiv

LIST OF ABBREVIATIONS xvi

1 INTRODUCTION 1

1.1 Background of the Study 1

1.2 Problem Statement / Motivation 3

1.3 Objective of Study 3

1.4 Significance of Study 4

1.5 Scope 4

1.6 Methodology 5

1.6.1 Instrumentation 5

1.6.2 Study Area 5

1.6.3 Outline of Methodology 6

1.7 Outline of Chapters 7

2 PHOTOGRAMMETRIC POINT CLOUD 11

2.1 Introduction 10

ix

2.2 Digital Photogrammetry 11

2.2.1 Direct Georeferencing 12

2.2.1.1 Automatic Point Measurement (APM) 12

2.2.1.2 Bundle Block Adjustment 13

2.2.2 Digital Image Matching 14

2.3 Photogrammetric Point Cloud Generation 15

2.4 Semi Global Matching Approach (SGM) 16

2.4.1 How Semi Global Matching Works 17

2.4.2 Digital Surface Models by Semi-Global

Matching

20

2.5 Photogrammetric Point Cloud to Digital Elevation

Model

22

2.5.1 Classification of Photogrammetric Point Cloud 22

2.5.2 Filtering of Photogrammetric Point Cloud 24

2.5.3 Surfacing 24

2.5.4 DEM Resolution 26

2.6 Accuracy Assessment For Photogrammetric Point Cloud

Generation & Extraction

27

2.6.1 Statistical Analysis of Root Mean Square Error 28

2.6.1.1 Vertical Accuracy Based on RMSE

Analysis

29

2.6.2 Statistical Analysis of the F-test 30

2.7 Summary 30

3 RESEARCH METHODOLOGHY 32

3.1 Introduction 32

3.2 Research Instruments 34

3.2.1 Photogrammetric Test Site 34

3.2.2 Photogrammetric Data 35

3.3 Data Collection 37

3.3.1 Aerial Imagery 37

3.3.1.1 Flight Planning 38

x

3.3.1.2 Aerial Digital Photo Acquisition 39

3.3.1.3 Position and Orientation (POS) Processing

and Workflow

40

3.3.1.4 Image Post Processing 41

3.3.1.5 Coordinate Transformation 41

3.3.2 Reference LiDAR Data 42

3.3.3 Ground Control Points 46

3.4 Data Processing (Digital Surface Model and Digital

Elevation Model Generation)

48

3.4.1 Automatic Aerial Triangulation 48

3.4.1.1 Work Methods 49

3.4.1.2 Results of Aerial Triangulation 51

3.4.2 Point Cloud Generation by Semi Global Matching 53

3.4.2.1 Work Methods 54

3.4.2.2 Results From Semi Global Matching

Process

55

3.4.3 Post Processing Point Cloud Data 58

3.4.3.1 Classification of Point Cloud 58

3.4.3.2 Filtering of Point Cloud 59

3.4.4 Digital Elevation Model Generation 61

3.4.4.1 Interpolation 62

3.5 Results & Data Analysis 63

3.5.1 Results 64

3.5.2 Quality Assurance of Photogrammetric Point

Cloud

66

3.5.2.1 Horizontal Accuracy Assessment 67

3.5.2.2 Vertical Accuracy Assessment 68

3.6 Summary 70

4 RESULT AND ANALYSIS 71

4.1 Introduction 71

4.2 Computing Vertical errors 72

4.2.1 Computation Vertical Error of Surface Height 73

xi

4.2.2 Computation Vertical Error of Terrain Height 74

4.3 Root Mean Square Error Computation 77

4.4 Quantitative Analysis 79

4.4.1 Comparative Height of DSM between SGM and

LiDAR

79

4.4.2 Comparative Height of DEM between SGM and

GCP’s

81

4.4.3 Comparative Height of DEM between LiDAR and

GCP’s

83

4.4.4 Comparative Height of between of DEM Model

from SGM and LiDAR

84

4.5 F-test 85

4.6 Error Analysis 86

4.7 Summary 91

5 CONCLUSION 92

5.1 Introduction 92

5.2 Conclusions 93

5.3 Recommendations 95

5.3.1 Recommendations Based on Results 95

5.3.2 Recommendations For Future Research 97

5.4 Final Remarks 98

REFERENCES 99

xii

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 ASPRS Standard LiDAR Point Classes 23

3.1 Parameters of TopoSys LiDAR. 43

3.2 Horizontal and Vertical Coordinate of Ground Control

Points

46

3.3 Relative Orientation result 52

3.4 Absolute Orientation result 53

3.5 Photogrammetric Point Clouds Metadata 57

3.6 Filter Point Cloud based on Classification Name 60

3.7 Filter Point Cloud based on Return Type 60

3.8 Surface Height from Photogrammtric Point Cloud data 65

3.9 Terrain Height from Photogrammtric Point Cloud data 65

3.10 Horizontal Coordinates of Check Points 67

3.11 Horizontal errors of Check Points 68

4.1 Vertical Error from DSM Computation Between SGM data

with LiDAR data

73

4.2 Vertical Error from DEM Computation Between SGM data

with Ground Control Points data

75

4.3 Vertical Error from DEM Computation Between SGM

Data with LiDAR Data

76

4.4 Vertical Error from DEM Computation Between LiDAR

Data with Ground Control Points Data

77

4.5 Root Mean Square Comparison Based on Method 77

xiii

Selection

4.6 Horizontal Accuracy of Photogrammetric Point Cloud 78

4.7 Comparison Height of Digital Surface Model between

SGM and LiDAR Dataset

81

4.8 Comparison Height of Digital Elevation Model between

SGM and Ground Control Points

82

4.9 Comparison Height of Digital Elevation Model between

LiDAR and Ground Control Points

83

4.10 Comparison Height of Digital Elevation Model between

SGM and LiDAR

85

4.11 F-test Two-Sample for Variances 86

xiv

LIST OF FIGURES

FIGURE

NO.

TITLE PAGE

1.1 Location map of Study Area 6

2.1 Calculations correlation between gray levels of pixels 13

2.2 Correlation at object border 16

2.3 Pathwise Optimization of SGM 18

2.4 Disparity Concept 21

3.1 Methodology Workflow of the Study 33

3.2 Test Field Location Map and Orthomosaic Image

Putrajaya, from DMC 0I Camera

34

3.3 Left, Digital Mapping Camera (DMC®), right, four MS

camera heads

35

3.4 Direct determination of camera positions and attitudes 36

3.5 Positional relationship between Camera, GNSS and IMU 36

3.6 Footprint of exposure station in Eastern direction 37

3.7 Mission Planning Workflows and Processes 38

3.8 Trimble POS AV image 39

3.9 Image Post Processing Workflows 41

3.10 LIDAR Flight Line for Putrajaya 42

3.11 Scan pattern of the TopoSys LIDAR 43

3.12 Airborne laser scanning scheme processing 44

3.13 Putrajaya Raster Digital Surface Model 45

xv

3.14 Putrajaya Raster Digital Elevation Model 45

3.15 The distribution of Ground Control Point, Putrajaya 46

3.16 Location of Ground Control Point on the Earth 47

3.17 Image to Ground 48

3.18 Automatic Aerial Triangulation by ISAT software 49

3.19 Parallax-X and Parallax-Y 51

3.20 Batch Script for XPro SGM 56

3.21 Semi-Global Matching Point Clouds 57

3.22 Raster DEM with 1 meter resolution per model 61

4.1 One of Surface Samples Checkpoints Generated from Semi

Global Matching Technique

80

4.2 One of Surface Samples Checkpoints Generated from

LIDAR Datasets

80

4.3 Ground Control Points Location on the Ground 82

4.4 Ground Control Points Monument on the Ground 82

4.5 LiDAR Multiple Return and Intensity 88

4.6 Orthomosaic Image Putrajaya, from DMC0I Camera 90

4.7 Orthomosaic Image Putrajaya, from Rollei Camera 90

xvi

LIST OF ABBREVIATIONS

ABBREVIATIONS TITLE

2D 2 Dimensional

3D 3 Dimensional

ABM Area Based Matching

APM Automatic Point Measurement

ASCII American Standard Code for Information Interchange

ASPRS American Society for Photogrammetry and Remote

Sensing

AT Aerial Triangulation

AAT Automatic Aerialt Triangulation

BSQ Band Sequential

CCD Charge Coupled Device

CVA Consolidated Vertical Accuracy

CORS Continues Reference Station

DEM Digital Elevation Model

DMC Digital Modular Camera

DSM Digital Surface Model

DTM Digital Terrain Model

EGM Earth Gravitational Model

EO Exterior Orientation

FBM Feature Based Matching

FVA Fundamental Vertical Accuracy

xvii

GCP Ground Control Point

GIS Geographical Information System

GSD Ground Sampling Distance

GPS Global Positioning System

ISAE-EXT ImageStation Automatic Elevation Extended

IMU Inertial Measurement Unit

INS Inertial Navigation System

IO Interior Orientation

ISAT ImageStation Automatic Triangulation

JPEG A lossy compression technique for color images

JUPEM Department of Survey and Mapping Malaysia

LAS Log ASCII Standard

LAZ Compressed Log ASCII Standard

LSM Least Square Matching

LiDAR Light, Detect and Ranging

MS Multi Spectral

MSL Mean Sea Level

NIR Near Infra-Red

NMAS National Map Accuracy Standard

NSSDA National Standard for Spatial Data Accuracy

NDVI Normalized Difference Vegetation Index

RGB Red, Green & Blue Band

RGBN Red, Green, Blue and Near Infra-Red Band

RM Relational Matching

RMS Root Mean Square

RMSD Root Mean Square Deviation

RMSE Root Mean Square Error

RTK Real-Time Kinematic

SAR Synthetic Aperture Radar

SCP Surface Control Points

SGM Semi Global Matching

SRTM Shuttle Radar Topography Mission

SVA Supplemental Vertical Accuracy

xviii

TIFF Tagged Image File Format

TIN Triangular Irregular Network

UAV Unmanned Aerial Vehicle

VMAS Vertical Map Accuracy Standard

1

CHAPTER 1

INTRODUCTION

1.1 Background of the Study

This study presents about the generation of Digital Elevation Model (DEM)

from the photogrammetric point cloud using Semi Global Matching (SGM)

approach. According to Hirschmüller (2005), SGM can be defined as the stereo

matching method that is founded on a pixel-wise matching, held by a global

smoothing function that is optimized along multiple paths. It holds a regular

algorithmic structure and uses simple operations and is thus well suited for parallel

implementation of the CPU using vector commands as well as on the GPU (Ernst

and Hirschmüller, 2008) and FPGA (Hirschmüller, 2011). It normally does not

require parameter tuning, this method is rather insensitive to the choice of

parameters.

DSM’s and DEM’s do play a major role in mapping purposes. At present,

several different methods are being available for DSM’s and DEM’s generation in

the mapping industry. Both of these elevation types can be generated from

topographic maps obtained by ground terrestrial laser measurements, airborne

2

photogrammetric data or remote sensing expertise. Based on our nation trend,

airborne LiDAR has become the key technology and an alternative to supply data for

DSMs and DEMs in the utmost few years in the mapping industry. Precise, high

density and high sampling 3D geometry data of the earth’s surface can be delivered

by airborne LiDAR. It’s been turning the choice method for large scale acquisition

of nationwide DEM.

Hitherto, motivation from digital Photogrammetric always impending into the

industry. For instance, the presence of a digital metric camera like ADS80, RCD30,

DMC, and Ultra-cam has been shown to construct multiple stereo coverage. And

then, the pixel base method is kept in DSM and DEM derivation. The innovation of

the existing stereo construction, it creates new data types, called as Photogrammetric

Point Cloud using Vision community technique, Semi Global Matching approach. In

some systems, SGM point clouds begin to return accuracy levels achieved by a

mobile LiDAR in open hard surfaces.

Hence, this study presents the SGM approach for DMC camera and compares

the results with LiDAR datasets and ground control points – in terms of DSM/DEM

resolution and accuracy. And likewise a question can be asked is which method

better than others.

1.3 Problem Statement / Motivation

Referable to the increasing of innovative technology in Photogrammetry now

and will continue ahead to impact the geospatial industry, SGM looks can replace

LiDAR with bargain pricing on point clouds. Multiple stereo coverage capabilities

of the digital frame camera are a fundamental of the pixel-based DSM/DEM

3

derivation where the image matching techniques at the present time be able to deliver

very dense point clouds. Given the situation, it is high affording that our mapping

agencies take serious effort in studying the goodness that may contributed from this

useful branch technology. Hence, this study is conducted to analyze the accuracy of

terrain from SGM technique, so that it can be recommended as an alternative source

of elevation besides as a primary choice for ultra-dense surface extraction images, in

case LiDAR technologies are not available and to save additional flight costs of the

department. Initial presumption is SGM accuracies are motivated by the

triangulation accuracies of the block imagery, typically 0.5 GSD horizontally and 1.5

GSD vertically.

1.4 Objective of Study

The objectives of this study are:

1. To study a workflow of generation Photogrammetric Point Cloud (Digital

Surface Model) data using Semi Global Matching approach.

2. To assess the accuracies of terrain elevation and surface height generated

from Semi Global Matching technique compared to airborne LiDAR data

and ground control point.

4

1.5 Significance of Study

Although currently in Malaysia there is no concrete evidence shows the

photogrammetric point clouds may leads to digital elevation data generation, the

findings of the study are important to prove that this type of elevation data can be

bottom next after LiDAR technologies or might be proposed as alternative if we

don’t have LiDAR data in the department. For instance, in recent years, we can see

an increasing natural disasters in our countries such as floods, earthquakes, landslides

so on. In further disaster analysis scenarios, it’s significant to know the conditions

before, current and after the disaster came so we can compare and do .real-time

updating it to horizontal and vertical conditions following requirements. Imagine

that if we don’t have LiDAR data from earlier? By having this product, 3D True

Ortho model can be shaped and from that we can quantify the alteration between two

temporal datasets from the Photogrammetric Point Cloud.

1.6 Scope

The focus of this project is to identify the workflow of DSM generation,

SGM and DEM extraction from photogrammetric technique, from an existing post

processing LiDAR technique. Other techniques in the creation of DSM and DEM

will not discuss in this study. The data of this survey were confined to large-format

airborne digital frame camera, Digital Modular Camera (metric-camera), which are

flown 2500 above mean sea level and it’s having the capability to make more than

60% stereo overlapping per models. Once the data is ready, it will be processed

using digital photogrammetric workflows and digital image matching technique will

be necessary along the generating of DSM. Stereo environment is replaced by point

cloud profile editing and extraction in order to generate raster DEM. Point’s interval

5

of point cloud is 18 cm which is same with the spatial resolution of aerial

photograph. LiDAR dataset and ground control points (GCP’s) are utilized as

reference data for this project.

1.7 Methodology

1.7.1 Instrumentation

The instrumentation for this study was involved hardware and software

applications. The hardware components include Digital Modular Camera (DMC 01)

and GNSS/IMU equipment (Trimble POS AV), whereas the processing software,

including Z/I Mission Planning, Z/I Inflight, Z/I Post Processing Software (PPS),

Waypoint Inertial Explorer, ImageStation Family Product, ERDAS Imagine

Professional and Geomedia GRID.

1.7.2 Study Area

The study area is an administrative center of Malaysia. Located in Putrajaya,

Malaysia, the study area extended by land features like vegetation area, water bodies,

residential area and open spaces. The base height of study area is about 15 meters to

110 meters above sea level. Because the conditions of terrain, features surrounding

this area besides the reference data for this study are available is a key factor the

research area is preferred.

6

Figure 1.1 Location map of Study Area

(Source: Google Map, 2015)

1.7.3 Outline of Methodology

In brief, the general sequences that will be involved in this study are:

a) Flight Mission Planning uses the Z/I Mission Planning. A mission

planning is designed based on terrain, and conditions of the research field.

b) Field Data Acquisition that involved image data capturing using DMC01

camera and GPS/INS recording using Trimble POS AV. Z/I Inflight

software is used to record the exposure stations information during the

mission.

c) Post-processing images using the Z/I Post Processing Software to output

out of RGB images

7

d) GPS/IMU Data Processing uses Waypoint Inertial Explorer software to

generate Exterior Orientation ((EO) parameter that consisted East, North,

Height, Omega, Phi and Kappa values.

e) Aerial triangulation process for registration and orientation of exposure

stations by ImageStation Automatic Triangulation software.

f) Point cloud generation using a SGM technique by ImageStation

Automatic Elevation Extended software.

g) 3D Viewing of Photogrammetric Point Cloud.

h) DEM extraction and generation by performing the classification, filtering,

and manual editing of point clouds, and interpolation of terrain elevation.

i) Quality Assurance with LiDAR and ground control points.

j) Present the result through the table’s analysis.

The detail explanation every methodology sequence in this study will be discussed in

Chapter 3, Research Methodology.

1.8 Outline of Chapters

In short, Chapter 1 exposes to the research proposal that present a clear

knowledge of the subject and explain the methodology. Chapter 1 starts with a

background study that concerned about the ways of the DSM and DEM generation

from Photogrammetry techniques, followed by a section of the different contents of

proposal, namely, problem statement or motivation, objective of the study, the

significance of the study and scope. The chapter proceeds with a methodology

process that the author plans to use to collect the data that substantiate the thesis

8

statement and achieved the stated aims. The chapter ends with the outline of the

methodology and outline of chapters to summarize the thesis action.

In Chapter 2, some note and literature reviews from previous study or

journals that highlighted about the SGM approach in order to generate a point cloud

or DSM that conducted by other researchers will be reviewed. Also, in this chapter

attached a theoretical base of the research to illustrate and justify the motivation of

this study from existing studies. This chapter ends with the summary of the DSM

and DEM generation, verification questions to integrate to the next chapter.

Chapter 3 demonstrates a research methodology on how to gather aerial

photogrammetric data for this research. This chapter begins with an introduction of

research methodology, followed by elaboration of the steps in general

Photogrammetry data methodology workflow, which include the data collection that

involved mission planning and field data acquisition, airborne data processing which

are separated by two parts namely, post processing images and GPS / IMU

processing. Then, followed by data processing in order to achieve the first objective

which are involved project management, aerial triangulation and bundle block

adjustment, point cloud generation by SGM approach and ended with DEM

extraction by editing DSM. When all workflows are completed, data analysis and

quality assurance will be executed. The chapter also covers the discussion, research

instruments from airborne to ground that involved hardware and software used along

with this subject area. Simple briefing about supporting data, LiDAR and GCPs

were explained in this chapter.

Chapter 4 will focus on result and analysis description. The chapter starts

with the introduction section which sum up the aim, previous chapter response and

DSM discussion. The chapter continues with the height analysis of surface and

terrain, which a core section of project writing. The description of height analysis

embraces the background information, explanation of results, positive or negative

9

support, contradict reference to previous research and suggesting general hypotheses

and implications based on research findings. In clearly and effectively result

presentation, the illustration and diagrams will be employed to demonstrate the

findings besides statement analysis. The chapter ends with the summary of the short

height analysis and review the findings.

Lastly, the emphasis on Chapter 5 is about making conclusion of the research

findings. According to another chapter, this chapter will begins with an intro,

accompanied by an overview of the summary of findings and end with the

recommendations based on findings and for future research. Some other element in

conclusion of this research includes restatement of the objectives, implications of

findings, limitations of the research and recommendations and suggestions for the

following studies.

99

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