3D urban object change detection from aerial and terrestrial point clouds: A review
Change detection has been increasingly studied in remote and close-range sensing in the last decades, driven by its importance in environment monitoring and database updating. Due to the development of sensing technologies, data acquisition has become more accessible and affordable and thus more dat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000808 |
| _version_ | 1797842842480017408 |
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| author | Wen Xiao Hui Cao Miao Tang Zhenchao Zhang Nengcheng Chen |
| author_facet | Wen Xiao Hui Cao Miao Tang Zhenchao Zhang Nengcheng Chen |
| author_sort | Wen Xiao |
| collection | DOAJ |
| description | Change detection has been increasingly studied in remote and close-range sensing in the last decades, driven by its importance in environment monitoring and database updating. Due to the development of sensing technologies, data acquisition has become more accessible and affordable and thus more data from various sensing platforms have become available. Thanks to structure-from-motion photogrammetry and lidar technologies, 3D change detection from point cloud data is drawing considerable attention in recent years. Motivated by the lack of a comprehensive review of 3D change detection in the urban environment, this paper reviews the latest developments in urban object change detection using point cloud data. In particular, four types of objects, namely building, street scene, urban tree, and construction site, are analysed in-depth. The use of different data sources for each object-of-interest and the open-source data with change labels are summarised. Then the change detection methods are thoroughly reviewed at pixel, point, voxel, segment and object levels, whose pros and cons are analysed in detail. Moreover, the challenges and opportunities brought by point cloud data and new methods, such as Siamese network deep learning, are discussed for future considerations. |
| first_indexed | 2024-04-09T16:54:48Z |
| format | Article |
| id | doaj.art-6edee3447a1a4505a86c615671ae5287 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T16:54:48Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-6edee3447a1a4505a86c615671ae52872023-04-21T06:41:13ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-04-011181032583D urban object change detection from aerial and terrestrial point clouds: A reviewWen Xiao0Hui Cao1Miao Tang2Zhenchao Zhang3Nengcheng Chen4School of Geography and Information Engineering, China University of Geosciences, 430074 Wuhan, China; National Engineering Research Center of Geographic Information System, China University of Geosciences, 430074 Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, 430074 Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, 430074 Wuhan, China; Corresponding author.Institute of Geospatial Information, Information Engineering University, 450001 Zhengzhou, ChinaNational Engineering Research Center of Geographic Information System, China University of Geosciences, 430074 Wuhan, ChinaChange detection has been increasingly studied in remote and close-range sensing in the last decades, driven by its importance in environment monitoring and database updating. Due to the development of sensing technologies, data acquisition has become more accessible and affordable and thus more data from various sensing platforms have become available. Thanks to structure-from-motion photogrammetry and lidar technologies, 3D change detection from point cloud data is drawing considerable attention in recent years. Motivated by the lack of a comprehensive review of 3D change detection in the urban environment, this paper reviews the latest developments in urban object change detection using point cloud data. In particular, four types of objects, namely building, street scene, urban tree, and construction site, are analysed in-depth. The use of different data sources for each object-of-interest and the open-source data with change labels are summarised. Then the change detection methods are thoroughly reviewed at pixel, point, voxel, segment and object levels, whose pros and cons are analysed in detail. Moreover, the challenges and opportunities brought by point cloud data and new methods, such as Siamese network deep learning, are discussed for future considerations.http://www.sciencedirect.com/science/article/pii/S1569843223000808Point cloudLidarSfM photogrammetryBuilding changeStreet sceneUrban tree |
| spellingShingle | Wen Xiao Hui Cao Miao Tang Zhenchao Zhang Nengcheng Chen 3D urban object change detection from aerial and terrestrial point clouds: A review International Journal of Applied Earth Observations and Geoinformation Point cloud Lidar SfM photogrammetry Building change Street scene Urban tree |
| title | 3D urban object change detection from aerial and terrestrial point clouds: A review |
| title_full | 3D urban object change detection from aerial and terrestrial point clouds: A review |
| title_fullStr | 3D urban object change detection from aerial and terrestrial point clouds: A review |
| title_full_unstemmed | 3D urban object change detection from aerial and terrestrial point clouds: A review |
| title_short | 3D urban object change detection from aerial and terrestrial point clouds: A review |
| title_sort | 3d urban object change detection from aerial and terrestrial point clouds a review |
| topic | Point cloud Lidar SfM photogrammetry Building change Street scene Urban tree |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000808 |
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