Revision and reconstruction of 3D building data by integrating starimager/tls imagery and complementary data

Masafumi Nakagawa, Ryosuke Shibasaki

Research output: Contribution to journalConference article

Abstract

Change extraction of building is needed to revise building data effectively. Many change detection algorithms use height difference analysis using temporal data such as LIDAR. However, remarkable changes of buildings cannot be detected in urban dense areas. On the other hand, building textures might change with higher possibility. However, the change cannot be detected due to influences of shadows and occlusion caused by nearby buildings in urban dense areas. Therefore, we have proposed a method to revise 3D building data by integrating texture change (roofs and walls) and 3D shape change of buildings using STARIMAGER/TLS (Three Line Sensor).

Original languageEnglish
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume35
Publication statusPublished - 2004 Jan 1
Externally publishedYes
Event20th ISPRS Congress on Technical Commission VII - Istanbul, Turkey
Duration: 2004 Jul 122004 Jul 23

Fingerprint

building
reconstruction
imagery
urban area
Textures
texture
temporal analysis
Roofs
roof
sensor
Sensors
detection
method

Keywords

  • 3D Mapping
  • Change detection
  • Data revision
  • Digital photogrammetry
  • TLS(Three Line Sensor)

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

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title = "Revision and reconstruction of 3D building data by integrating starimager/tls imagery and complementary data",
abstract = "Change extraction of building is needed to revise building data effectively. Many change detection algorithms use height difference analysis using temporal data such as LIDAR. However, remarkable changes of buildings cannot be detected in urban dense areas. On the other hand, building textures might change with higher possibility. However, the change cannot be detected due to influences of shadows and occlusion caused by nearby buildings in urban dense areas. Therefore, we have proposed a method to revise 3D building data by integrating texture change (roofs and walls) and 3D shape change of buildings using STARIMAGER/TLS (Three Line Sensor).",
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N2 - Change extraction of building is needed to revise building data effectively. Many change detection algorithms use height difference analysis using temporal data such as LIDAR. However, remarkable changes of buildings cannot be detected in urban dense areas. On the other hand, building textures might change with higher possibility. However, the change cannot be detected due to influences of shadows and occlusion caused by nearby buildings in urban dense areas. Therefore, we have proposed a method to revise 3D building data by integrating texture change (roofs and walls) and 3D shape change of buildings using STARIMAGER/TLS (Three Line Sensor).

AB - Change extraction of building is needed to revise building data effectively. Many change detection algorithms use height difference analysis using temporal data such as LIDAR. However, remarkable changes of buildings cannot be detected in urban dense areas. On the other hand, building textures might change with higher possibility. However, the change cannot be detected due to influences of shadows and occlusion caused by nearby buildings in urban dense areas. Therefore, we have proposed a method to revise 3D building data by integrating texture change (roofs and walls) and 3D shape change of buildings using STARIMAGER/TLS (Three Line Sensor).

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