Improved building facade segmentation through digital twin-enabled RandLA-Net with empirical intensity correction model Article Swipe
Michael Bekele Maru
,
Yusen Wang
,
Hansun Kim
,
Hyungchul Yoon
,
Seunghee Park
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jobe.2023.107520
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jobe.2023.107520
The Architectural Engineering and Construction (AEC) industry can benefit from accurate building facade segmentation, which can provide valuable insights into building maintenance, urban planning, and security efforts. Light Detection and Ranging (LiDAR) sensors are effective in recognizing building facade components from (three-dimensional) 3D point clouds by registering 3D spatial coordinates and radiometric information, which reveal the spectral property of a scanned surface. Although the radiometric information (i.e., intensity feature) can be used for segmentation, its accuracy may be reduced by factors such as scanning geometry and external factors that affect the object's radiometric information. To address this issue, this study proposes a robust and automated method for segmenting building facade components using LiDAR point cloud data and an empirical-based intensity correction model to ensure proper segmentation. The proposed method employs RandLA-Net, a deep learning model capable of effectively processing large-scale point cloud data, to classify building facade components based on their spatial features combined with corrected intensity features. By incorporating the proposed method into a digital twin, it is possible to perform accurate building facade segmentation and generate valuable insights into the building's physical condition, energy efficiency, and aesthetic value in real-time. The effectiveness of the proposed method was experimentally validated using a school building facade, which demonstrated significant improvements in the recognition of facade components and highlighted the potential of digital twin-enabled building facade segmentation for the AEC industry.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jobe.2023.107520
- OA Status
- hybrid
- Cited By
- 24
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385652415
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385652415Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.jobe.2023.107520Digital Object Identifier
- Title
-
Improved building facade segmentation through digital twin-enabled RandLA-Net with empirical intensity correction modelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-09Full publication date if available
- Authors
-
Michael Bekele Maru, Yusen Wang, Hansun Kim, Hyungchul Yoon, Seunghee ParkList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jobe.2023.107520Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.jobe.2023.107520Direct OA link when available
- Concepts
-
Facade, Point cloud, Segmentation, Computer science, Lidar, Artificial intelligence, Spatial analysis, Ranging, Building model, Feature (linguistics), Computer vision, Remote sensing, Engineering, Simulation, Geography, Civil engineering, Philosophy, Telecommunications, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385652415 |
|---|---|
| doi | https://doi.org/10.1016/j.jobe.2023.107520 |
| ids.doi | https://doi.org/10.1016/j.jobe.2023.107520 |
| ids.openalex | https://openalex.org/W4385652415 |
| fwci | 12.59755104 |
| type | article |
| title | Improved building facade segmentation through digital twin-enabled RandLA-Net with empirical intensity correction model |
| biblio.issue | |
| biblio.volume | 78 |
| biblio.last_page | 107520 |
| biblio.first_page | 107520 |
| topics[0].id | https://openalex.org/T11211 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1907 |
| topics[0].subfield.display_name | Geology |
| topics[0].display_name | 3D Surveying and Cultural Heritage |
| topics[1].id | https://openalex.org/T11606 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9980999827384949 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2205 |
| topics[1].subfield.display_name | Civil and Structural Engineering |
| topics[1].display_name | Infrastructure Maintenance and Monitoring |
| topics[2].id | https://openalex.org/T11164 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9979000091552734 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2305 |
| topics[2].subfield.display_name | Environmental Engineering |
| topics[2].display_name | Remote Sensing and LiDAR Applications |
| funders[0].id | https://openalex.org/F4320322120 |
| funders[0].ror | https://ror.org/013aysd81 |
| funders[0].display_name | National Research Foundation of Korea |
| is_xpac | False |
| apc_list.value | 3050 |
| apc_list.currency | USD |
| apc_list.value_usd | 3050 |
| apc_paid.value | 3050 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3050 |
| concepts[0].id | https://openalex.org/C2780113678 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9827497005462646 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q183061 |
| concepts[0].display_name | Facade |
| concepts[1].id | https://openalex.org/C131979681 |
| concepts[1].level | 2 |
| concepts[1].score | 0.763826847076416 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[1].display_name | Point cloud |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7249681949615479 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6220950484275818 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C51399673 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5447616577148438 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q504027 |
| concepts[4].display_name | Lidar |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5050638318061829 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C159620131 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46673285961151123 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1938983 |
| concepts[6].display_name | Spatial analysis |
| concepts[7].id | https://openalex.org/C115051666 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46151161193847656 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q6522493 |
| concepts[7].display_name | Ranging |
| concepts[8].id | https://openalex.org/C2778530916 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44395142793655396 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1352349 |
| concepts[8].display_name | Building model |
| concepts[9].id | https://openalex.org/C2776401178 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4240879714488983 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[9].display_name | Feature (linguistics) |
| concepts[10].id | https://openalex.org/C31972630 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3628547787666321 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[10].display_name | Computer vision |
| concepts[11].id | https://openalex.org/C62649853 |
| concepts[11].level | 1 |
| concepts[11].score | 0.26446428894996643 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[11].display_name | Remote sensing |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.21702975034713745 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C44154836 |
| concepts[13].level | 1 |
| concepts[13].score | 0.17627409100532532 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q45045 |
| concepts[13].display_name | Simulation |
| concepts[14].id | https://openalex.org/C205649164 |
| concepts[14].level | 0 |
| concepts[14].score | 0.12463721632957458 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[14].display_name | Geography |
| concepts[15].id | https://openalex.org/C147176958 |
| concepts[15].level | 1 |
| concepts[15].score | 0.11475411057472229 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q77590 |
| concepts[15].display_name | Civil engineering |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C76155785 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[17].display_name | Telecommunications |
| concepts[18].id | https://openalex.org/C41895202 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[18].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/facade |
| keywords[0].score | 0.9827497005462646 |
| keywords[0].display_name | Facade |
| keywords[1].id | https://openalex.org/keywords/point-cloud |
| keywords[1].score | 0.763826847076416 |
| keywords[1].display_name | Point cloud |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.7249681949615479 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6220950484275818 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/lidar |
| keywords[4].score | 0.5447616577148438 |
| keywords[4].display_name | Lidar |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.5050638318061829 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/spatial-analysis |
| keywords[6].score | 0.46673285961151123 |
| keywords[6].display_name | Spatial analysis |
| keywords[7].id | https://openalex.org/keywords/ranging |
| keywords[7].score | 0.46151161193847656 |
| keywords[7].display_name | Ranging |
| keywords[8].id | https://openalex.org/keywords/building-model |
| keywords[8].score | 0.44395142793655396 |
| keywords[8].display_name | Building model |
| keywords[9].id | https://openalex.org/keywords/feature |
| keywords[9].score | 0.4240879714488983 |
| keywords[9].display_name | Feature (linguistics) |
| keywords[10].id | https://openalex.org/keywords/computer-vision |
| keywords[10].score | 0.3628547787666321 |
| keywords[10].display_name | Computer vision |
| keywords[11].id | https://openalex.org/keywords/remote-sensing |
| keywords[11].score | 0.26446428894996643 |
| keywords[11].display_name | Remote sensing |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.21702975034713745 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/simulation |
| keywords[13].score | 0.17627409100532532 |
| keywords[13].display_name | Simulation |
| keywords[14].id | https://openalex.org/keywords/geography |
| keywords[14].score | 0.12463721632957458 |
| keywords[14].display_name | Geography |
| keywords[15].id | https://openalex.org/keywords/civil-engineering |
| keywords[15].score | 0.11475411057472229 |
| keywords[15].display_name | Civil engineering |
| language | en |
| locations[0].id | doi:10.1016/j.jobe.2023.107520 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764478331 |
| locations[0].source.issn | 2352-7102 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2352-7102 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Building Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Journal of Building Engineering |
| locations[0].landing_page_url | https://doi.org/10.1016/j.jobe.2023.107520 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5072927888 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1917-349X |
| authorships[0].author.display_name | Michael Bekele Maru |
| authorships[0].countries | KR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I848706 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[0].institutions[0].id | https://openalex.org/I848706 |
| authorships[0].institutions[0].ror | https://ror.org/04q78tk20 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I848706 |
| authorships[0].institutions[0].country_code | KR |
| authorships[0].institutions[0].display_name | Sungkyunkwan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Michael Bekele Maru |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[1].author.id | https://openalex.org/A5101695613 |
| authorships[1].author.orcid | https://orcid.org/0009-0006-0341-9562 |
| authorships[1].author.display_name | Yusen Wang |
| authorships[1].countries | KR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I848706 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[1].institutions[0].id | https://openalex.org/I848706 |
| authorships[1].institutions[0].ror | https://ror.org/04q78tk20 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I848706 |
| authorships[1].institutions[0].country_code | KR |
| authorships[1].institutions[0].display_name | Sungkyunkwan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yusen Wang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[2].author.id | https://openalex.org/A5070981998 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Hansun Kim |
| authorships[2].countries | KR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I848706 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Global Smart City, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[2].institutions[0].id | https://openalex.org/I848706 |
| authorships[2].institutions[0].ror | https://ror.org/04q78tk20 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I848706 |
| authorships[2].institutions[0].country_code | KR |
| authorships[2].institutions[0].display_name | Sungkyunkwan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hansun Kim |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Global Smart City, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[3].author.id | https://openalex.org/A5090646352 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2558-6303 |
| authorships[3].author.display_name | Hyungchul Yoon |
| authorships[3].countries | KR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I163753206 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Civil Engineering, Chungbuk National University, Cheongju 28644, South Korea |
| authorships[3].institutions[0].id | https://openalex.org/I163753206 |
| authorships[3].institutions[0].ror | https://ror.org/02wnxgj78 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I163753206 |
| authorships[3].institutions[0].country_code | KR |
| authorships[3].institutions[0].display_name | Chungbuk National University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hyungchul Yoon |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Civil Engineering, Chungbuk National University, Cheongju 28644, South Korea |
| authorships[4].author.id | https://openalex.org/A5101805904 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6265-7652 |
| authorships[4].author.display_name | Seunghee Park |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I848706 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| authorships[4].institutions[0].id | https://openalex.org/I848706 |
| authorships[4].institutions[0].ror | https://ror.org/04q78tk20 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I848706 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Sungkyunkwan University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Seunghee Park |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | School of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, South Korea |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.jobe.2023.107520 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Improved building facade segmentation through digital twin-enabled RandLA-Net with empirical intensity correction model |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11211 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1907 |
| primary_topic.subfield.display_name | Geology |
| primary_topic.display_name | 3D Surveying and Cultural Heritage |
| related_works | https://openalex.org/W194964019, https://openalex.org/W4283708567, https://openalex.org/W2787642737, https://openalex.org/W2990937015, https://openalex.org/W117643219, https://openalex.org/W9113147, https://openalex.org/W2043297143, https://openalex.org/W2953923365, https://openalex.org/W1982485721, https://openalex.org/W4381571749 |
| cited_by_count | 24 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 16 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.jobe.2023.107520 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764478331 |
| best_oa_location.source.issn | 2352-7102 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2352-7102 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of Building Engineering |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Journal of Building Engineering |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.jobe.2023.107520 |
| primary_location.id | doi:10.1016/j.jobe.2023.107520 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764478331 |
| primary_location.source.issn | 2352-7102 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2352-7102 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Building Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Journal of Building Engineering |
| primary_location.landing_page_url | https://doi.org/10.1016/j.jobe.2023.107520 |
| publication_date | 2023-08-09 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2883908928, https://openalex.org/W4224903638, https://openalex.org/W6847848031, https://openalex.org/W6796189141, https://openalex.org/W6771001627, https://openalex.org/W6832724496, https://openalex.org/W2980114529, https://openalex.org/W2922073063, https://openalex.org/W6791882774, https://openalex.org/W1972521474, https://openalex.org/W2128880484, https://openalex.org/W2349179172, https://openalex.org/W2017585657, https://openalex.org/W1998624933, https://openalex.org/W3208474673, https://openalex.org/W6795307729, https://openalex.org/W4312339689, https://openalex.org/W6849486090, https://openalex.org/W6648864392, https://openalex.org/W4225751528, https://openalex.org/W3084505175, https://openalex.org/W3039448353, https://openalex.org/W6646913887, https://openalex.org/W2995932445, https://openalex.org/W1984579500, https://openalex.org/W1978781796, https://openalex.org/W6794752790, https://openalex.org/W2086461898, https://openalex.org/W1239585782, https://openalex.org/W2302538754, https://openalex.org/W2149788694, https://openalex.org/W6714087844, https://openalex.org/W2291054725, https://openalex.org/W1993457928, https://openalex.org/W2163478179, https://openalex.org/W6600589122, https://openalex.org/W6646872678, https://openalex.org/W6666587646, https://openalex.org/W6655160902, https://openalex.org/W2134185806, https://openalex.org/W6633622824, https://openalex.org/W2054772870, https://openalex.org/W6648672272, https://openalex.org/W2807328492, https://openalex.org/W4319764016, https://openalex.org/W2801573647, https://openalex.org/W6676424343, https://openalex.org/W3176374653, https://openalex.org/W2113551198, https://openalex.org/W3012494314, https://openalex.org/W2892374446, https://openalex.org/W191494004, https://openalex.org/W3140331270, https://openalex.org/W4365799847, https://openalex.org/W3163698194, https://openalex.org/W3162787701, https://openalex.org/W4365800070, https://openalex.org/W2491807451, https://openalex.org/W4241307704, https://openalex.org/W3135264477, https://openalex.org/W2405964867, https://openalex.org/W4240191558, https://openalex.org/W4313200712, https://openalex.org/W4301264315, https://openalex.org/W4233721802, https://openalex.org/W3159695470 |
| referenced_works_count | 66 |
| abstract_inverted_index.a | 59, 101, 131, 164, 202 |
| abstract_inverted_index.3D | 42, 47 |
| abstract_inverted_index.By | 158 |
| abstract_inverted_index.To | 94 |
| abstract_inverted_index.an | 117 |
| abstract_inverted_index.as | 82 |
| abstract_inverted_index.be | 70, 77 |
| abstract_inverted_index.by | 45, 79 |
| abstract_inverted_index.in | 35, 190, 210 |
| abstract_inverted_index.is | 168 |
| abstract_inverted_index.it | 167 |
| abstract_inverted_index.of | 58, 136, 194, 213, 220 |
| abstract_inverted_index.on | 149 |
| abstract_inverted_index.to | 122, 143, 170 |
| abstract_inverted_index.AEC | 228 |
| abstract_inverted_index.The | 0, 126, 192 |
| abstract_inverted_index.and | 3, 24, 29, 50, 85, 103, 116, 176, 187, 216 |
| abstract_inverted_index.are | 33 |
| abstract_inverted_index.can | 7, 15, 69 |
| abstract_inverted_index.for | 72, 106, 226 |
| abstract_inverted_index.its | 74 |
| abstract_inverted_index.may | 76 |
| abstract_inverted_index.the | 55, 63, 90, 160, 181, 195, 211, 218, 227 |
| abstract_inverted_index.was | 198 |
| abstract_inverted_index.data | 115 |
| abstract_inverted_index.deep | 132 |
| abstract_inverted_index.from | 9, 40 |
| abstract_inverted_index.into | 19, 163, 180 |
| abstract_inverted_index.such | 81 |
| abstract_inverted_index.that | 88 |
| abstract_inverted_index.this | 96, 98 |
| abstract_inverted_index.used | 71 |
| abstract_inverted_index.with | 154 |
| abstract_inverted_index.(AEC) | 5 |
| abstract_inverted_index.LiDAR | 112 |
| abstract_inverted_index.Light | 27 |
| abstract_inverted_index.based | 148 |
| abstract_inverted_index.cloud | 114, 141 |
| abstract_inverted_index.data, | 142 |
| abstract_inverted_index.model | 121, 134 |
| abstract_inverted_index.point | 43, 113, 140 |
| abstract_inverted_index.study | 99 |
| abstract_inverted_index.their | 150 |
| abstract_inverted_index.twin, | 166 |
| abstract_inverted_index.urban | 22 |
| abstract_inverted_index.using | 111, 201 |
| abstract_inverted_index.value | 189 |
| abstract_inverted_index.which | 14, 53, 206 |
| abstract_inverted_index.(i.e., | 66 |
| abstract_inverted_index.affect | 89 |
| abstract_inverted_index.clouds | 44 |
| abstract_inverted_index.energy | 185 |
| abstract_inverted_index.ensure | 123 |
| abstract_inverted_index.facade | 12, 38, 109, 146, 174, 214, 224 |
| abstract_inverted_index.issue, | 97 |
| abstract_inverted_index.method | 105, 128, 162, 197 |
| abstract_inverted_index.proper | 124 |
| abstract_inverted_index.reveal | 54 |
| abstract_inverted_index.robust | 102 |
| abstract_inverted_index.school | 203 |
| abstract_inverted_index.(LiDAR) | 31 |
| abstract_inverted_index.Ranging | 30 |
| abstract_inverted_index.address | 95 |
| abstract_inverted_index.benefit | 8 |
| abstract_inverted_index.capable | 135 |
| abstract_inverted_index.digital | 165, 221 |
| abstract_inverted_index.employs | 129 |
| abstract_inverted_index.facade, | 205 |
| abstract_inverted_index.factors | 80, 87 |
| abstract_inverted_index.perform | 171 |
| abstract_inverted_index.provide | 16 |
| abstract_inverted_index.reduced | 78 |
| abstract_inverted_index.scanned | 60 |
| abstract_inverted_index.sensors | 32 |
| abstract_inverted_index.spatial | 48, 151 |
| abstract_inverted_index.Although | 62 |
| abstract_inverted_index.accuracy | 75 |
| abstract_inverted_index.accurate | 10, 172 |
| abstract_inverted_index.building | 11, 20, 37, 108, 145, 173, 204, 223 |
| abstract_inverted_index.classify | 144 |
| abstract_inverted_index.combined | 153 |
| abstract_inverted_index.efforts. | 26 |
| abstract_inverted_index.external | 86 |
| abstract_inverted_index.feature) | 68 |
| abstract_inverted_index.features | 152 |
| abstract_inverted_index.generate | 177 |
| abstract_inverted_index.geometry | 84 |
| abstract_inverted_index.industry | 6 |
| abstract_inverted_index.insights | 18, 179 |
| abstract_inverted_index.learning | 133 |
| abstract_inverted_index.object's | 91 |
| abstract_inverted_index.physical | 183 |
| abstract_inverted_index.possible | 169 |
| abstract_inverted_index.property | 57 |
| abstract_inverted_index.proposed | 127, 161, 196 |
| abstract_inverted_index.proposes | 100 |
| abstract_inverted_index.scanning | 83 |
| abstract_inverted_index.security | 25 |
| abstract_inverted_index.spectral | 56 |
| abstract_inverted_index.surface. | 61 |
| abstract_inverted_index.valuable | 17, 178 |
| abstract_inverted_index.Detection | 28 |
| abstract_inverted_index.aesthetic | 188 |
| abstract_inverted_index.automated | 104 |
| abstract_inverted_index.corrected | 155 |
| abstract_inverted_index.effective | 34 |
| abstract_inverted_index.features. | 157 |
| abstract_inverted_index.industry. | 229 |
| abstract_inverted_index.intensity | 67, 119, 156 |
| abstract_inverted_index.planning, | 23 |
| abstract_inverted_index.potential | 219 |
| abstract_inverted_index.validated | 200 |
| abstract_inverted_index.building's | 182 |
| abstract_inverted_index.components | 39, 110, 147, 215 |
| abstract_inverted_index.condition, | 184 |
| abstract_inverted_index.correction | 120 |
| abstract_inverted_index.processing | 138 |
| abstract_inverted_index.real-time. | 191 |
| abstract_inverted_index.segmenting | 107 |
| abstract_inverted_index.Engineering | 2 |
| abstract_inverted_index.RandLA-Net, | 130 |
| abstract_inverted_index.coordinates | 49 |
| abstract_inverted_index.effectively | 137 |
| abstract_inverted_index.efficiency, | 186 |
| abstract_inverted_index.highlighted | 217 |
| abstract_inverted_index.information | 65 |
| abstract_inverted_index.large-scale | 139 |
| abstract_inverted_index.radiometric | 51, 64, 92 |
| abstract_inverted_index.recognition | 212 |
| abstract_inverted_index.recognizing | 36 |
| abstract_inverted_index.registering | 46 |
| abstract_inverted_index.significant | 208 |
| abstract_inverted_index.Construction | 4 |
| abstract_inverted_index.demonstrated | 207 |
| abstract_inverted_index.improvements | 209 |
| abstract_inverted_index.information, | 52 |
| abstract_inverted_index.information. | 93 |
| abstract_inverted_index.maintenance, | 21 |
| abstract_inverted_index.segmentation | 175, 225 |
| abstract_inverted_index.twin-enabled | 222 |
| abstract_inverted_index.Architectural | 1 |
| abstract_inverted_index.effectiveness | 193 |
| abstract_inverted_index.incorporating | 159 |
| abstract_inverted_index.segmentation, | 13, 73 |
| abstract_inverted_index.segmentation. | 125 |
| abstract_inverted_index.experimentally | 199 |
| abstract_inverted_index.empirical-based | 118 |
| abstract_inverted_index.(three-dimensional) | 41 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101805904 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I848706 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.98555011 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |