3

3D Building Reconstruction

• GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS, pages 3–10

3D City Models

• A DATA STRUCTURE TO INCORPORATE VERSIONING IN 3D CITY MODELS, pages 123–130

3D GIS

• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING, pages 27–34

3D LiDAR point clouds

• AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH, pages 139–146

3D Modelling

• INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN, pages 147–154

3D Objects

• INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN, pages 147–154

3D Visibility Analysis

• VOXEL-BASED VISIBILITY ANALYSIS FOR SAFETY ASSESSMENT OF URBAN ENVIRONMENTS, pages 11–17

3D building models

• A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS, pages 51–57

3D building reconstruction

• A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS, pages 51–57

3D city models

• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING, pages 27–34
• AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS, pages 75–82

3D point cloud

• AUTOMATIC 2D MODELLING OF INNER ROOF PLANES BOUNDARIES STARTING FROM LIDAR DATA, pages 107–114

3D urban scene

• AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH, pages 139–146

A

Active Sampling

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

Asset Management

• DEVELOPING IFC FOR INFRASTRUCTURE: A CASE STUDY OF THREE HIGHWAY ENTITIES, pages 59–66

Automated Driving

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

airborne Lidar

• CITY RECONSTRUCTION FROM AIRBORNE LIDAR: A COMPUTATIONAL GEOMETRY APPROACH, pages 19–26

attributes

• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90

B

BIM

• QUALITY OF BIM–GIS CONVERSION, pages 35–42
• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90
• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

Big Data

• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

Building

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

Building Footprint Extraction

• GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS, pages 3–10

Building Information Modeling

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74

C

CNN

• GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS, pages 3–10

CityGML

• QUALITY OF BIM–GIS CONVERSION, pages 35–42
• AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS, pages 75–82
• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90
• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106
• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122
• A DATA STRUCTURE TO INCORPORATE VERSIONING IN 3D CITY MODELS, pages 123–130

CityJSON

• A DATA STRUCTURE TO INCORPORATE VERSIONING IN 3D CITY MODELS, pages 123–130

classification

• CITY RECONSTRUCTION FROM AIRBORNE LIDAR: A COMPUTATIONAL GEOMETRY APPROACH, pages 19–26

conversion

• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90

D

Deep Learning

• GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS, pages 3–10

Digital Twins

• INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN, pages 147–154

Distributed Computing

• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

domain-specific language

• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

E

Entities

• DEVELOPING IFC FOR INFRASTRUCTURE: A CASE STUDY OF THREE HIGHWAY ENTITIES, pages 59–66

Environment

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

Extraction

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98

errors

• QUALITY OF BIM–GIS CONVERSION, pages 35–42

G

GIS

• QUALITY OF BIM–GIS CONVERSION, pages 35–42
• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90
• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

Georeferencing

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74

Git

• A DATA STRUCTURE TO INCORPORATE VERSIONING IN 3D CITY MODELS, pages 123–130

graph transformation

• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90
• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

H

Highway

• DEVELOPING IFC FOR INFRASTRUCTURE: A CASE STUDY OF THREE HIGHWAY ENTITIES, pages 59–66

I

IFC

• QUALITY OF BIM–GIS CONVERSION, pages 35–42
• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74
• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90
• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

Industry Foundation Classes (IFC)

• DEVELOPING IFC FOR INFRASTRUCTURE: A CASE STUDY OF THREE HIGHWAY ENTITIES, pages 59–66

Infrastructure

• DEVELOPING IFC FOR INFRASTRUCTURE: A CASE STUDY OF THREE HIGHWAY ENTITIES, pages 59–66

Integration

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74
• INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN, pages 147–154

K

kinetic data structures

• CITY RECONSTRUCTION FROM AIRBORNE LIDAR: A COMPUTATIONAL GEOMETRY APPROACH, pages 19–26

L

LOD framework

• AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS, pages 75–82

Landscape Objects

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74

Level of Detail

• AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS, pages 75–82

LiDAR

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98
• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

Lidar

• AUTOMATIC 2D MODELLING OF INNER ROOF PLANES BOUNDARIES STARTING FROM LIDAR DATA, pages 107–114

LoD2

• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING, pages 27–34

Location Plan

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74

M

Machine Learning

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

Modelling

• AUTOMATIC 2D MODELLING OF INNER ROOF PLANES BOUNDARIES STARTING FROM LIDAR DATA, pages 107–114

machine learning

• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING, pages 27–34

O

OpenDRIVE

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

open data

• A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS, pages 51–57

P

Planes boundaries

• AUTOMATIC 2D MODELLING OF INNER ROOF PLANES BOUNDARIES STARTING FROM LIDAR DATA, pages 107–114

Point Cloud

• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

Power line corridor

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98

point clouds

• CITY RECONSTRUCTION FROM AIRBORNE LIDAR: A COMPUTATIONAL GEOMETRY APPROACH, pages 19–26

properties

• GRAPH TRANSFORMATION RULES FOR IFC-TO-CITYGML ATTRIBUTE CONVERSION, pages 83–90

pylon

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98

Q

quality

• QUALITY OF BIM–GIS CONVERSION, pages 35–42

quality of 3D data

• A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS, pages 51–57

R

Reconstruction

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

Requirements

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

Road Space

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

Roof

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

roof

• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING, pages 27–34

roof classification

• A MULTI-HEIGHT LOD1 MODEL OF ALL BUILDINGS IN THE NETHERLANDS, pages 51–57

S

SVM

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98

Safety

• VOXEL-BASED VISIBILITY ANALYSIS FOR SAFETY ASSESSMENT OF URBAN ENVIRONMENTS, pages 11–17

Segmentation

• AUTOMATIC 2D MODELLING OF INNER ROOF PLANES BOUNDARIES STARTING FROM LIDAR DATA, pages 107–114

Semantic Segmentation

• GENERATING 3D CITY MODELS BASED ON THE SEMANTIC SEGMENTATION OF LIDAR DATA USING CONVOLUTIONAL NEURAL NETWORKS, pages 3–10

Semantic segmentation

• AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH, pages 139–146

Sensors

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

Shadow Analysis

• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

Street

• REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING, pages 99–106

Surflet

• ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS, pages 43–50

Surveying

• 3D LANDSCAPE OBJECTS FOR BUILDING INFORMATION MODELS (BIM), pages 67–74

T

Terrain

• INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN, pages 147–154

terrain

• AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS, pages 75–82

topology

• QUALITY OF BIM–GIS CONVERSION, pages 35–42

triple graph grammar

• CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES, pages 115–122

U

Urban Environment

• VOXEL-BASED VISIBILITY ANALYSIS FOR SAFETY ASSESSMENT OF URBAN ENVIRONMENTS, pages 11–17

Urban Shadow

• A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS, pages 131–137

urban reconstruction

• CITY RECONSTRUCTION FROM AIRBORNE LIDAR: A COMPUTATIONAL GEOMETRY APPROACH, pages 19–26

V

Versioning

• A DATA STRUCTURE TO INCORPORATE VERSIONING IN 3D CITY MODELS, pages 123–130

Visualisation

• VOXEL-BASED VISIBILITY ANALYSIS FOR SAFETY ASSESSMENT OF URBAN ENVIRONMENTS, pages 11–17

Voxels

• VOXEL-BASED VISIBILITY ANALYSIS FOR SAFETY ASSESSMENT OF URBAN ENVIRONMENTS, pages 11–17

validation

• QUALITY OF BIM–GIS CONVERSION, pages 35–42

W

wires

• VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA, pages 91–98