Innovative Forensic Features and Blood Spatter 3D Reconstruction from Geospatial-driven Datasets Article Swipe
Ahmad Firdaus Razali
,
Kyna Lani Edward
,
Mohd Farid Mohd Ariff
,
Nurul Shahirah Jasni
,
Norhadija Darwin
,
Zulkepli Majid
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48084/etasr.10327
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48084/etasr.10327
Crime scene documentation serves as the basis for reconstructing and interpreting evidence to provide forensic evaluation of crime events. The reliance on 2D photography for forensic features does not employ extensive documentation, as it limits the analytical properties of the captured data, such as 3D data fusion accurate scaling and innovative augmentation. This paper presents an innovative method to reconstruct forensic features and blood spatter using geospatial-driven datasets from multiple sources. The procedure involves a 3D reconstruction of a crime scene via geospatial techniques, namely Close-Range Photogrammetry (CRP) using an iPhone X smartphone camera and Terrestrial Laser Scanning (TLS) using a Faro Focus laser scanner. Meta Quest 2 was then used to transform the TLS point cloud into an immersive visualization. Meanwhile, an integrated point cloud model, transformed by the Iterative Closest Point (ICP) algorithm, was produced to examine the texture quality of the features being scanned, especially in blood spatter details. To determine the accuracy, the dimension measurements of the pieces of evidence were evaluated and analyzed using a two-sample t-test. The geometric property was extracted, showing that there were no significant differences between the mean-dimension measurements for all point samples. Additionally, the RGB values extracted from the point cloud datasets showed that CRP contributed to higher values compared to TLS. This shows that CRP facilitates more precise bloodstain pattern analysis compared to TLS.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.48084/etasr.10327
- https://etasr.com/index.php/ETASR/article/download/10327/5103
- OA Status
- gold
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411122690
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411122690Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48084/etasr.10327Digital Object Identifier
- Title
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Innovative Forensic Features and Blood Spatter 3D Reconstruction from Geospatial-driven DatasetsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-04Full publication date if available
- Authors
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Ahmad Firdaus Razali, Kyna Lani Edward, Mohd Farid Mohd Ariff, Nurul Shahirah Jasni, Norhadija Darwin, Zulkepli MajidList of authors in order
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https://doi.org/10.48084/etasr.10327Publisher landing page
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https://etasr.com/index.php/ETASR/article/download/10327/5103Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://etasr.com/index.php/ETASR/article/download/10327/5103Direct OA link when available
- Concepts
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Geospatial analysis, Forensic science, Data mining, Computer science, Data science, Pattern recognition (psychology), Artificial intelligence, Geology, Geography, Remote sensing, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.multiple | 69 |
| abstract_inverted_index.presents | 54 |
| abstract_inverted_index.produced | 136 |
| abstract_inverted_index.property | 174 |
| abstract_inverted_index.reliance | 20 |
| abstract_inverted_index.samples. | 191 |
| abstract_inverted_index.scanned, | 146 |
| abstract_inverted_index.scanner. | 104 |
| abstract_inverted_index.sources. | 70 |
| abstract_inverted_index.Iterative | 130 |
| abstract_inverted_index.accuracy, | 155 |
| abstract_inverted_index.determine | 153 |
| abstract_inverted_index.dimension | 157 |
| abstract_inverted_index.evaluated | 165 |
| abstract_inverted_index.extensive | 30 |
| abstract_inverted_index.extracted | 196 |
| abstract_inverted_index.geometric | 173 |
| abstract_inverted_index.immersive | 119 |
| abstract_inverted_index.procedure | 72 |
| abstract_inverted_index.transform | 112 |
| abstract_inverted_index.Meanwhile, | 121 |
| abstract_inverted_index.algorithm, | 134 |
| abstract_inverted_index.analytical | 36 |
| abstract_inverted_index.bloodstain | 219 |
| abstract_inverted_index.especially | 147 |
| abstract_inverted_index.evaluation | 15 |
| abstract_inverted_index.extracted, | 176 |
| abstract_inverted_index.geospatial | 82 |
| abstract_inverted_index.innovative | 50, 56 |
| abstract_inverted_index.integrated | 123 |
| abstract_inverted_index.properties | 37 |
| abstract_inverted_index.smartphone | 92 |
| abstract_inverted_index.two-sample | 170 |
| abstract_inverted_index.Close-Range | 85 |
| abstract_inverted_index.Terrestrial | 95 |
| abstract_inverted_index.contributed | 205 |
| abstract_inverted_index.differences | 183 |
| abstract_inverted_index.facilitates | 216 |
| abstract_inverted_index.photography | 23 |
| abstract_inverted_index.reconstruct | 59 |
| abstract_inverted_index.significant | 182 |
| abstract_inverted_index.techniques, | 83 |
| abstract_inverted_index.transformed | 127 |
| abstract_inverted_index.interpreting | 10 |
| abstract_inverted_index.measurements | 158, 187 |
| abstract_inverted_index.Additionally, | 192 |
| abstract_inverted_index.augmentation. | 51 |
| abstract_inverted_index.documentation | 2 |
| abstract_inverted_index.Photogrammetry | 86 |
| abstract_inverted_index.documentation, | 31 |
| abstract_inverted_index.mean-dimension | 186 |
| abstract_inverted_index.reconstructing | 8 |
| abstract_inverted_index.reconstruction | 76 |
| abstract_inverted_index.visualization. | 120 |
| abstract_inverted_index.geospatial-driven | 66 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.80006296 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |