Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information Article Swipe
F. Politz
,
Monika Sester
,
Claus Brenner
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/agile-giss-2-10-2021
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/agile-giss-2-10-2021
Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As detecting changes manually is very time consuming, the aim of this study is to provide reliable change detections for different building sizes in order to support NMAs in their task to update their databases. As datasets of different times may have varying point densities due to technological advancements or different sensors, we propose a raster-based approach, which is independent of the point density altogether. Within a raster cell, our approach considers the height distribution of all points for two points in time by exploiting the Jensen-Shannon distance to measure their similarity. Our proposed method outperforms simple threshold methods on detecting building changes with respect to the same or different point cloud types. In combination with our proposed class change detection approach, we achieve a change detection performance measured by the mean F1-Score of about 71% between two ALS and about 60% between ALS and DIM point clouds acquired at different times.
Related Topics
Concepts
Point cloud
Raster graphics
Change detection
Computer science
Matching (statistics)
Context (archaeology)
Artificial intelligence
Remote sensing
Similarity (geometry)
Computer vision
Lidar
Point (geometry)
Pattern recognition (psychology)
Image (mathematics)
Geography
Mathematics
Statistics
Geometry
Archaeology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/agile-giss-2-10-2021
- https://agile-giss.copernicus.org/articles/2/10/2021/agile-giss-2-10-2021.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3168809494
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3168809494Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/agile-giss-2-10-2021Digital Object Identifier
- Title
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Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class InformationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-04Full publication date if available
- Authors
-
F. Politz, Monika Sester, Claus BrennerList of authors in order
- Landing page
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https://doi.org/10.5194/agile-giss-2-10-2021Publisher landing page
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https://agile-giss.copernicus.org/articles/2/10/2021/agile-giss-2-10-2021.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://agile-giss.copernicus.org/articles/2/10/2021/agile-giss-2-10-2021.pdfDirect OA link when available
- Concepts
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Point cloud, Raster graphics, Change detection, Computer science, Matching (statistics), Context (archaeology), Artificial intelligence, Remote sensing, Similarity (geometry), Computer vision, Lidar, Point (geometry), Pattern recognition (psychology), Image (mathematics), Geography, Mathematics, Statistics, Geometry, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_10_percent | False |