IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA Article Swipe
Pablo del Río-Barral
,
Javier Grandío
,
B. Riveiro
,
Pedro Arias
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023
The maintenance of road infrastructures is one of the main challenges that transportation authorities must face to guarantee the safe mobility of people and goods. Novel remote monitoring technologies offer advanced solutions for this issue, allowing the inspection of large sections of the network in a time-effective way. In this paper, we introduce a methodology for the detection of cracks on road pavements using point clouds acquired with a mobile laser scanner. First, the points of the cloud are labelled as pavement or cracks based on field annotations, and local geometric features of the points are calculated using principal component analysis. Two different machine learning classifiers, Support Vector Machine (SVM) and Random Forest, are then trained to identify crack points using the point feature data. The crack points predicted by the classifiers are clustered as individual instances and compared to the corresponding ones from a test dataset. Although pointwise performance of the method is modest, it can correctly identify and measure areas of the pavement affected by cracking.
Related Topics
Concepts
Point cloud
Computer science
Pointwise
Support vector machine
Point (geometry)
Identification (biology)
Feature (linguistics)
Random forest
Laser scanning
Artificial intelligence
Cloud computing
Face (sociological concept)
Field (mathematics)
Data mining
Measure (data warehouse)
Pattern recognition (psychology)
Mathematics
Geometry
Laser
Social science
Mathematical analysis
Sociology
Biology
Operating system
Pure mathematics
Optics
Botany
Physics
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023
- https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378194565
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378194565Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023Digital Object Identifier
- Title
-
IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATAWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-25Full publication date if available
- Authors
-
Pablo del Río-Barral, Javier Grandío, B. Riveiro, Pedro AriasList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023Publisher landing page
- PDF URL
-
https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdfDirect OA link when available
- Concepts
-
Point cloud, Computer science, Pointwise, Support vector machine, Point (geometry), Identification (biology), Feature (linguistics), Random forest, Laser scanning, Artificial intelligence, Cloud computing, Face (sociological concept), Field (mathematics), Data mining, Measure (data warehouse), Pattern recognition (psychology), Mathematics, Geometry, Laser, Social science, Mathematical analysis, Sociology, Biology, Operating system, Pure mathematics, Optics, Botany, Physics, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4378194565 |
|---|---|
| doi | https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| ids.doi | https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| ids.openalex | https://openalex.org/W4378194565 |
| fwci | 0.61352095 |
| type | article |
| title | IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA |
| biblio.issue | |
| biblio.volume | XLVIII-1/W1-2023 |
| biblio.last_page | 112 |
| biblio.first_page | 107 |
| topics[0].id | https://openalex.org/T11606 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2205 |
| topics[0].subfield.display_name | Civil and Structural Engineering |
| topics[0].display_name | Infrastructure Maintenance and Monitoring |
| topics[1].id | https://openalex.org/T11164 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9865999817848206 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Remote Sensing and LiDAR Applications |
| topics[2].id | https://openalex.org/T11211 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9860000014305115 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1907 |
| topics[2].subfield.display_name | Geology |
| topics[2].display_name | 3D Surveying and Cultural Heritage |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C131979681 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8949410915374756 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[0].display_name | Point cloud |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6177284121513367 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2777984123 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6070559024810791 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q9248237 |
| concepts[2].display_name | Pointwise |
| concepts[3].id | https://openalex.org/C12267149 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5795347094535828 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[3].display_name | Support vector machine |
| concepts[4].id | https://openalex.org/C28719098 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5605928301811218 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[4].display_name | Point (geometry) |
| concepts[5].id | https://openalex.org/C116834253 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5483173131942749 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[5].display_name | Identification (biology) |
| concepts[6].id | https://openalex.org/C2776401178 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5416290163993835 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[6].display_name | Feature (linguistics) |
| concepts[7].id | https://openalex.org/C169258074 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4629310965538025 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[7].display_name | Random forest |
| concepts[8].id | https://openalex.org/C141349535 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4614803194999695 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1361664 |
| concepts[8].display_name | Laser scanning |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4608478248119354 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C79974875 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4495231509208679 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[10].display_name | Cloud computing |
| concepts[11].id | https://openalex.org/C2779304628 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4402807652950287 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q3503480 |
| concepts[11].display_name | Face (sociological concept) |
| concepts[12].id | https://openalex.org/C9652623 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4245489835739136 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[12].display_name | Field (mathematics) |
| concepts[13].id | https://openalex.org/C124101348 |
| concepts[13].level | 1 |
| concepts[13].score | 0.4202570617198944 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[13].display_name | Data mining |
| concepts[14].id | https://openalex.org/C2780009758 |
| concepts[14].level | 2 |
| concepts[14].score | 0.4152681827545166 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q6804172 |
| concepts[14].display_name | Measure (data warehouse) |
| concepts[15].id | https://openalex.org/C153180895 |
| concepts[15].level | 2 |
| concepts[15].score | 0.327336847782135 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[15].display_name | Pattern recognition (psychology) |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.1567823886871338 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| concepts[17].id | https://openalex.org/C2524010 |
| concepts[17].level | 1 |
| concepts[17].score | 0.08735883235931396 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[17].display_name | Geometry |
| concepts[18].id | https://openalex.org/C520434653 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q38867 |
| concepts[18].display_name | Laser |
| concepts[19].id | https://openalex.org/C36289849 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[19].display_name | Social science |
| concepts[20].id | https://openalex.org/C134306372 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[20].display_name | Mathematical analysis |
| concepts[21].id | https://openalex.org/C144024400 |
| concepts[21].level | 0 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[21].display_name | Sociology |
| concepts[22].id | https://openalex.org/C86803240 |
| concepts[22].level | 0 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[22].display_name | Biology |
| concepts[23].id | https://openalex.org/C111919701 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[23].display_name | Operating system |
| concepts[24].id | https://openalex.org/C202444582 |
| concepts[24].level | 1 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[24].display_name | Pure mathematics |
| concepts[25].id | https://openalex.org/C120665830 |
| concepts[25].level | 1 |
| concepts[25].score | 0.0 |
| concepts[25].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[25].display_name | Optics |
| concepts[26].id | https://openalex.org/C59822182 |
| concepts[26].level | 1 |
| concepts[26].score | 0.0 |
| concepts[26].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[26].display_name | Botany |
| concepts[27].id | https://openalex.org/C121332964 |
| concepts[27].level | 0 |
| concepts[27].score | 0.0 |
| concepts[27].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[27].display_name | Physics |
| concepts[28].id | https://openalex.org/C138885662 |
| concepts[28].level | 0 |
| concepts[28].score | 0.0 |
| concepts[28].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[28].display_name | Philosophy |
| concepts[29].id | https://openalex.org/C41895202 |
| concepts[29].level | 1 |
| concepts[29].score | 0.0 |
| concepts[29].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[29].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/point-cloud |
| keywords[0].score | 0.8949410915374756 |
| keywords[0].display_name | Point cloud |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6177284121513367 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/pointwise |
| keywords[2].score | 0.6070559024810791 |
| keywords[2].display_name | Pointwise |
| keywords[3].id | https://openalex.org/keywords/support-vector-machine |
| keywords[3].score | 0.5795347094535828 |
| keywords[3].display_name | Support vector machine |
| keywords[4].id | https://openalex.org/keywords/point |
| keywords[4].score | 0.5605928301811218 |
| keywords[4].display_name | Point (geometry) |
| keywords[5].id | https://openalex.org/keywords/identification |
| keywords[5].score | 0.5483173131942749 |
| keywords[5].display_name | Identification (biology) |
| keywords[6].id | https://openalex.org/keywords/feature |
| keywords[6].score | 0.5416290163993835 |
| keywords[6].display_name | Feature (linguistics) |
| keywords[7].id | https://openalex.org/keywords/random-forest |
| keywords[7].score | 0.4629310965538025 |
| keywords[7].display_name | Random forest |
| keywords[8].id | https://openalex.org/keywords/laser-scanning |
| keywords[8].score | 0.4614803194999695 |
| keywords[8].display_name | Laser scanning |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.4608478248119354 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/cloud-computing |
| keywords[10].score | 0.4495231509208679 |
| keywords[10].display_name | Cloud computing |
| keywords[11].id | https://openalex.org/keywords/face |
| keywords[11].score | 0.4402807652950287 |
| keywords[11].display_name | Face (sociological concept) |
| keywords[12].id | https://openalex.org/keywords/field |
| keywords[12].score | 0.4245489835739136 |
| keywords[12].display_name | Field (mathematics) |
| keywords[13].id | https://openalex.org/keywords/data-mining |
| keywords[13].score | 0.4202570617198944 |
| keywords[13].display_name | Data mining |
| keywords[14].id | https://openalex.org/keywords/measure |
| keywords[14].score | 0.4152681827545166 |
| keywords[14].display_name | Measure (data warehouse) |
| keywords[15].id | https://openalex.org/keywords/pattern-recognition |
| keywords[15].score | 0.327336847782135 |
| keywords[15].display_name | Pattern recognition (psychology) |
| keywords[16].id | https://openalex.org/keywords/mathematics |
| keywords[16].score | 0.1567823886871338 |
| keywords[16].display_name | Mathematics |
| keywords[17].id | https://openalex.org/keywords/geometry |
| keywords[17].score | 0.08735883235931396 |
| keywords[17].display_name | Geometry |
| language | en |
| locations[0].id | doi:10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2737215817 |
| locations[0].source.issn | 1682-1750, 1682-1777, 2194-9034 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1682-1750 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| locations[0].source.host_organization | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_name | Copernicus Publications |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_lineage_names | Copernicus Publications |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| locations[0].landing_page_url | https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| locations[1].id | pmh:oai:doaj.org/article:81c0a30f30a04cb391a80feb2b2493ad |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-1-W1-2023, Pp 107-112 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/81c0a30f30a04cb391a80feb2b2493ad |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5025850966 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9733-7683 |
| authorships[0].author.display_name | Pablo del Río-Barral |
| authorships[0].countries | ES |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[0].affiliations[0].raw_affiliation_string | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[0].institutions[0].id | https://openalex.org/I6289922 |
| authorships[0].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[0].institutions[0].country_code | ES |
| authorships[0].institutions[0].display_name | Universidade de Vigo |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | P. del Río-Barral |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[1].author.id | https://openalex.org/A5042937616 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5384-7975 |
| authorships[1].author.display_name | Javier Grandío |
| authorships[1].countries | ES |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[1].affiliations[0].raw_affiliation_string | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[1].institutions[0].id | https://openalex.org/I6289922 |
| authorships[1].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[1].institutions[0].country_code | ES |
| authorships[1].institutions[0].display_name | Universidade de Vigo |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | J. Grandío |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[2].author.id | https://openalex.org/A5016720746 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1497-4370 |
| authorships[2].author.display_name | B. Riveiro |
| authorships[2].countries | ES |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[2].affiliations[0].raw_affiliation_string | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[2].institutions[0].id | https://openalex.org/I6289922 |
| authorships[2].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[2].institutions[0].country_code | ES |
| authorships[2].institutions[0].display_name | Universidade de Vigo |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | B. Riveiro |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[3].author.id | https://openalex.org/A5002336145 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3547-8907 |
| authorships[3].author.display_name | Pedro Arias |
| authorships[3].countries | ES |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[3].affiliations[0].raw_affiliation_string | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| authorships[3].institutions[0].id | https://openalex.org/I6289922 |
| authorships[3].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[3].institutions[0].country_code | ES |
| authorships[3].institutions[0].display_name | Universidade de Vigo |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | P. Arias |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | CINTECX, Universidade de Vigo, GeoTECH Group, Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11606 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2205 |
| primary_topic.subfield.display_name | Civil and Structural Engineering |
| primary_topic.display_name | Infrastructure Maintenance and Monitoring |
| related_works | https://openalex.org/W4200112873, https://openalex.org/W2955796858, https://openalex.org/W4224941037, https://openalex.org/W2088131065, https://openalex.org/W2005998065, https://openalex.org/W2004826645, https://openalex.org/W2001220299, https://openalex.org/W4323518558, https://openalex.org/W2286704396, https://openalex.org/W3135818052 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2737215817 |
| best_oa_location.source.issn | 1682-1750, 1682-1777, 2194-9034 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1682-1750 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| best_oa_location.source.host_organization | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_name | Copernicus Publications |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_lineage_names | Copernicus Publications |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| primary_location.id | doi:10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2737215817 |
| primary_location.source.issn | 1682-1750, 1682-1777, 2194-9034 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1682-1750 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences |
| primary_location.source.host_organization | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_name | Copernicus Publications |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_lineage_names | Copernicus Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/107/2023/isprs-archives-XLVIII-1-W1-2023-107-2023.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| primary_location.landing_page_url | https://doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-107-2023 |
| publication_date | 2023-05-25 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3014123961, https://openalex.org/W2008488233, https://openalex.org/W2144801789, https://openalex.org/W2079054397, https://openalex.org/W2107272822, https://openalex.org/W2407692387, https://openalex.org/W3005933934, https://openalex.org/W2964308596, https://openalex.org/W2899242765, https://openalex.org/W2748643398, https://openalex.org/W3011200270, https://openalex.org/W2608996180, https://openalex.org/W2765772691, https://openalex.org/W2594490116, https://openalex.org/W3186879668, https://openalex.org/W2521928520, https://openalex.org/W3012494314, https://openalex.org/W2103820944, https://openalex.org/W2132239597, https://openalex.org/W4246299849, https://openalex.org/W3011741193, https://openalex.org/W3137826854, https://openalex.org/W2031319648, https://openalex.org/W1565456886, https://openalex.org/W6637872014, https://openalex.org/W2101801592, https://openalex.org/W6719677100, https://openalex.org/W2480429758, https://openalex.org/W2099346911, https://openalex.org/W2083122430, https://openalex.org/W2467615243, https://openalex.org/W1756736144 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 46, 54, 69, 145 |
| abstract_inverted_index.In | 49 |
| abstract_inverted_index.as | 81, 135 |
| abstract_inverted_index.by | 130, 167 |
| abstract_inverted_index.in | 45 |
| abstract_inverted_index.is | 6, 154 |
| abstract_inverted_index.it | 156 |
| abstract_inverted_index.of | 3, 8, 22, 39, 42, 59, 76, 93, 151, 163 |
| abstract_inverted_index.on | 61, 86 |
| abstract_inverted_index.or | 83 |
| abstract_inverted_index.to | 17, 117, 140 |
| abstract_inverted_index.we | 52 |
| abstract_inverted_index.The | 1, 126 |
| abstract_inverted_index.Two | 102 |
| abstract_inverted_index.and | 24, 89, 111, 138, 160 |
| abstract_inverted_index.are | 79, 96, 114, 133 |
| abstract_inverted_index.can | 157 |
| abstract_inverted_index.for | 33, 56 |
| abstract_inverted_index.one | 7 |
| abstract_inverted_index.the | 9, 19, 37, 43, 57, 74, 77, 94, 122, 131, 141, 152, 164 |
| abstract_inverted_index.face | 16 |
| abstract_inverted_index.from | 144 |
| abstract_inverted_index.main | 10 |
| abstract_inverted_index.must | 15 |
| abstract_inverted_index.ones | 143 |
| abstract_inverted_index.road | 4, 62 |
| abstract_inverted_index.safe | 20 |
| abstract_inverted_index.test | 146 |
| abstract_inverted_index.that | 12 |
| abstract_inverted_index.then | 115 |
| abstract_inverted_index.this | 34, 50 |
| abstract_inverted_index.way. | 48 |
| abstract_inverted_index.with | 68 |
| abstract_inverted_index.(SVM) | 110 |
| abstract_inverted_index.Novel | 26 |
| abstract_inverted_index.areas | 162 |
| abstract_inverted_index.based | 85 |
| abstract_inverted_index.cloud | 78 |
| abstract_inverted_index.crack | 119, 127 |
| abstract_inverted_index.data. | 125 |
| abstract_inverted_index.field | 87 |
| abstract_inverted_index.large | 40 |
| abstract_inverted_index.laser | 71 |
| abstract_inverted_index.local | 90 |
| abstract_inverted_index.offer | 30 |
| abstract_inverted_index.point | 65, 123 |
| abstract_inverted_index.using | 64, 98, 121 |
| abstract_inverted_index.First, | 73 |
| abstract_inverted_index.Random | 112 |
| abstract_inverted_index.Vector | 108 |
| abstract_inverted_index.clouds | 66 |
| abstract_inverted_index.cracks | 60, 84 |
| abstract_inverted_index.goods. | 25 |
| abstract_inverted_index.issue, | 35 |
| abstract_inverted_index.method | 153 |
| abstract_inverted_index.mobile | 70 |
| abstract_inverted_index.paper, | 51 |
| abstract_inverted_index.people | 23 |
| abstract_inverted_index.points | 75, 95, 120, 128 |
| abstract_inverted_index.remote | 27 |
| abstract_inverted_index.Forest, | 113 |
| abstract_inverted_index.Machine | 109 |
| abstract_inverted_index.Support | 107 |
| abstract_inverted_index.feature | 124 |
| abstract_inverted_index.machine | 104 |
| abstract_inverted_index.measure | 161 |
| abstract_inverted_index.modest, | 155 |
| abstract_inverted_index.network | 44 |
| abstract_inverted_index.trained | 116 |
| abstract_inverted_index.Although | 148 |
| abstract_inverted_index.acquired | 67 |
| abstract_inverted_index.advanced | 31 |
| abstract_inverted_index.affected | 166 |
| abstract_inverted_index.allowing | 36 |
| abstract_inverted_index.compared | 139 |
| abstract_inverted_index.dataset. | 147 |
| abstract_inverted_index.features | 92 |
| abstract_inverted_index.identify | 118, 159 |
| abstract_inverted_index.labelled | 80 |
| abstract_inverted_index.learning | 105 |
| abstract_inverted_index.mobility | 21 |
| abstract_inverted_index.pavement | 82, 165 |
| abstract_inverted_index.scanner. | 72 |
| abstract_inverted_index.sections | 41 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.analysis. | 101 |
| abstract_inverted_index.clustered | 134 |
| abstract_inverted_index.component | 100 |
| abstract_inverted_index.correctly | 158 |
| abstract_inverted_index.cracking. | 168 |
| abstract_inverted_index.detection | 58 |
| abstract_inverted_index.different | 103 |
| abstract_inverted_index.geometric | 91 |
| abstract_inverted_index.guarantee | 18 |
| abstract_inverted_index.instances | 137 |
| abstract_inverted_index.introduce | 53 |
| abstract_inverted_index.pavements | 63 |
| abstract_inverted_index.pointwise | 149 |
| abstract_inverted_index.predicted | 129 |
| abstract_inverted_index.principal | 99 |
| abstract_inverted_index.solutions | 32 |
| abstract_inverted_index.calculated | 97 |
| abstract_inverted_index.challenges | 11 |
| abstract_inverted_index.individual | 136 |
| abstract_inverted_index.inspection | 38 |
| abstract_inverted_index.monitoring | 28 |
| abstract_inverted_index.authorities | 14 |
| abstract_inverted_index.classifiers | 132 |
| abstract_inverted_index.maintenance | 2 |
| abstract_inverted_index.methodology | 55 |
| abstract_inverted_index.performance | 150 |
| abstract_inverted_index.annotations, | 88 |
| abstract_inverted_index.classifiers, | 106 |
| abstract_inverted_index.technologies | 29 |
| abstract_inverted_index.corresponding | 142 |
| abstract_inverted_index.time-effective | 47 |
| abstract_inverted_index.transportation | 13 |
| abstract_inverted_index.infrastructures | 5 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.61554151 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |