Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking Article Swipe
Kenneth Olofsson
,
Johan Holmgren
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14214/sf.10712
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14214/sf.10712
A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, QT, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (>99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14214/sf.10712
- https://silvafennica.fi/pdf/10712
- OA Status
- gold
- Cited By
- 3
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313010317
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313010317Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14214/sf.10712Digital Object Identifier
- Title
-
Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linkingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Kenneth Olofsson, Johan HolmgrenList of authors in order
- Landing page
-
https://doi.org/10.14214/sf.10712Publisher landing page
- PDF URL
-
https://silvafennica.fi/pdf/10712Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://silvafennica.fi/pdf/10712Direct OA link when available
- Concepts
-
Position (finance), Metric (unit), Tree (set theory), Computer science, k-nearest neighbors algorithm, Pattern recognition (psychology), Artificial intelligence, Detector, Mathematics, Statistics, Engineering, Economics, Telecommunications, Finance, Mathematical analysis, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4313010317 |
|---|---|
| doi | https://doi.org/10.14214/sf.10712 |
| ids.doi | https://doi.org/10.14214/sf.10712 |
| ids.openalex | https://openalex.org/W4313010317 |
| fwci | 0.29485311 |
| type | article |
| title | Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking |
| biblio.issue | 3 |
| biblio.volume | 56 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11164 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9973999857902527 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2305 |
| topics[0].subfield.display_name | Environmental Engineering |
| topics[0].display_name | Remote Sensing and LiDAR Applications |
| topics[1].id | https://openalex.org/T12644 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9632999897003174 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2303 |
| topics[1].subfield.display_name | Ecology |
| topics[1].display_name | Wildlife-Road Interactions and Conservation |
| topics[2].id | https://openalex.org/T10555 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9553999900817871 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Fire effects on ecosystems |
| is_xpac | False |
| apc_list.value | 800 |
| apc_list.currency | EUR |
| apc_list.value_usd | 862 |
| apc_paid.value | 800 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 862 |
| concepts[0].id | https://openalex.org/C198082294 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6514496803283691 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3399648 |
| concepts[0].display_name | Position (finance) |
| concepts[1].id | https://openalex.org/C176217482 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5693749785423279 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q860554 |
| concepts[1].display_name | Metric (unit) |
| concepts[2].id | https://openalex.org/C113174947 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5619290471076965 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[2].display_name | Tree (set theory) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.49006354808807373 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C113238511 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4855949282646179 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1071612 |
| concepts[4].display_name | k-nearest neighbors algorithm |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.44800621271133423 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43777233362197876 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C94915269 |
| concepts[7].level | 2 |
| concepts[7].score | 0.42409729957580566 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1834857 |
| concepts[7].display_name | Detector |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.4237360656261444 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C105795698 |
| concepts[9].level | 1 |
| concepts[9].score | 0.41030463576316833 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[9].display_name | Statistics |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.1195685863494873 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C162324750 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[11].display_name | Economics |
| concepts[12].id | https://openalex.org/C76155785 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[12].display_name | Telecommunications |
| concepts[13].id | https://openalex.org/C10138342 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q43015 |
| concepts[13].display_name | Finance |
| concepts[14].id | https://openalex.org/C134306372 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[14].display_name | Mathematical analysis |
| concepts[15].id | https://openalex.org/C21547014 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q1423657 |
| concepts[15].display_name | Operations management |
| keywords[0].id | https://openalex.org/keywords/position |
| keywords[0].score | 0.6514496803283691 |
| keywords[0].display_name | Position (finance) |
| keywords[1].id | https://openalex.org/keywords/metric |
| keywords[1].score | 0.5693749785423279 |
| keywords[1].display_name | Metric (unit) |
| keywords[2].id | https://openalex.org/keywords/tree |
| keywords[2].score | 0.5619290471076965 |
| keywords[2].display_name | Tree (set theory) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.49006354808807373 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/k-nearest-neighbors-algorithm |
| keywords[4].score | 0.4855949282646179 |
| keywords[4].display_name | k-nearest neighbors algorithm |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.44800621271133423 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.43777233362197876 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/detector |
| keywords[7].score | 0.42409729957580566 |
| keywords[7].display_name | Detector |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.4237360656261444 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/statistics |
| keywords[9].score | 0.41030463576316833 |
| keywords[9].display_name | Statistics |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.1195685863494873 |
| keywords[10].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.14214/sf.10712 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S114295916 |
| locations[0].source.issn | 0037-5330, 2242-4075 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0037-5330 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Silva Fennica |
| locations[0].source.host_organization | https://openalex.org/P4310317072 |
| locations[0].source.host_organization_name | Finnish Society of Forest Science |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310317072 |
| locations[0].source.host_organization_lineage_names | Finnish Society of Forest Science |
| locations[0].license | cc-by-sa |
| locations[0].pdf_url | https://silvafennica.fi/pdf/10712 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Silva Fennica |
| locations[0].landing_page_url | https://doi.org/10.14214/sf.10712 |
| locations[1].id | pmh:oai:doaj.org/article:18d1aadbab644b5eab5c0e6205718f43 |
| 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 | Silva Fennica, Vol 56, Iss 3 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/18d1aadbab644b5eab5c0e6205718f43 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5016283812 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2836-2316 |
| authorships[0].author.display_name | Kenneth Olofsson |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Kenneth Olofsson |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5002781320 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2566-4183 |
| authorships[1].author.display_name | Johan Holmgren |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Johan Holmgren |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://silvafennica.fi/pdf/10712 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11164 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9973999857902527 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2305 |
| primary_topic.subfield.display_name | Environmental Engineering |
| primary_topic.display_name | Remote Sensing and LiDAR Applications |
| related_works | https://openalex.org/W2146076056, https://openalex.org/W2167440101, https://openalex.org/W2166213322, https://openalex.org/W2883887418, https://openalex.org/W3203501097, https://openalex.org/W2130997612, https://openalex.org/W4286960226, https://openalex.org/W3021364800, https://openalex.org/W2040821848, https://openalex.org/W2058818547 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.14214/sf.10712 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S114295916 |
| best_oa_location.source.issn | 0037-5330, 2242-4075 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0037-5330 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Silva Fennica |
| best_oa_location.source.host_organization | https://openalex.org/P4310317072 |
| best_oa_location.source.host_organization_name | Finnish Society of Forest Science |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310317072 |
| best_oa_location.source.host_organization_lineage_names | Finnish Society of Forest Science |
| best_oa_location.license | cc-by-sa |
| best_oa_location.pdf_url | https://silvafennica.fi/pdf/10712 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-sa |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Silva Fennica |
| best_oa_location.landing_page_url | https://doi.org/10.14214/sf.10712 |
| primary_location.id | doi:10.14214/sf.10712 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S114295916 |
| primary_location.source.issn | 0037-5330, 2242-4075 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0037-5330 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Silva Fennica |
| primary_location.source.host_organization | https://openalex.org/P4310317072 |
| primary_location.source.host_organization_name | Finnish Society of Forest Science |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310317072 |
| primary_location.source.host_organization_lineage_names | Finnish Society of Forest Science |
| primary_location.license | cc-by-sa |
| primary_location.pdf_url | https://silvafennica.fi/pdf/10712 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-sa |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Silva Fennica |
| primary_location.landing_page_url | https://doi.org/10.14214/sf.10712 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2065367565, https://openalex.org/W2146881125, https://openalex.org/W2294137362, https://openalex.org/W2523569918, https://openalex.org/W2014428508, https://openalex.org/W2765739008, https://openalex.org/W2989883239, https://openalex.org/W4205283982, https://openalex.org/W2133160971, https://openalex.org/W3109077063, https://openalex.org/W3205476853, https://openalex.org/W1991540202, https://openalex.org/W2156067790, https://openalex.org/W2280788228, https://openalex.org/W2884197231, https://openalex.org/W1984982830, https://openalex.org/W2024244823, https://openalex.org/W4205542274, https://openalex.org/W2522686994, https://openalex.org/W2609825692, https://openalex.org/W1991873888, https://openalex.org/W4207008904, https://openalex.org/W2050576295 |
| referenced_works_count | 23 |
| abstract_inverted_index.A | 0, 57, 113, 149 |
| abstract_inverted_index.a | 31, 89, 133, 157 |
| abstract_inverted_index.m | 170 |
| abstract_inverted_index.an | 123 |
| abstract_inverted_index.as | 19, 21 |
| abstract_inverted_index.in | 10, 52, 132 |
| abstract_inverted_index.is | 25, 63 |
| abstract_inverted_index.it | 68 |
| abstract_inverted_index.m, | 173 |
| abstract_inverted_index.of | 6, 30, 129, 141, 178 |
| abstract_inverted_index.on | 44 |
| abstract_inverted_index.to | 17, 70, 88, 171 |
| abstract_inverted_index.QT, | 62 |
| abstract_inverted_index.The | 127 |
| abstract_inverted_index.and | 13, 74, 92, 102, 110, 144 |
| abstract_inverted_index.are | 86, 136 |
| abstract_inverted_index.can | 104 |
| abstract_inverted_index.for | 3 |
| abstract_inverted_index.has | 122 |
| abstract_inverted_index.map | 77 |
| abstract_inverted_index.new | 1 |
| abstract_inverted_index.the | 4, 39, 53, 120, 139, 162, 176 |
| abstract_inverted_index.two | 84 |
| abstract_inverted_index.0.51 | 172 |
| abstract_inverted_index.1.90 | 169 |
| abstract_inverted_index.This | 27 |
| abstract_inverted_index.also | 64 |
| abstract_inverted_index.case | 151 |
| abstract_inverted_index.data | 9, 23, 117, 155 |
| abstract_inverted_index.from | 156, 168 |
| abstract_inverted_index.high | 80, 99 |
| abstract_inverted_index.real | 153 |
| abstract_inverted_index.stem | 32, 49, 76, 142 |
| abstract_inverted_index.test | 114, 150 |
| abstract_inverted_index.that | 37, 96, 103, 119, 161 |
| abstract_inverted_index.this | 179 |
| abstract_inverted_index.tree | 8, 55 |
| abstract_inverted_index.well | 20 |
| abstract_inverted_index.were | 147 |
| abstract_inverted_index.when | 42 |
| abstract_inverted_index.with | 48, 79, 98, 107, 115, 152 |
| abstract_inverted_index.These | 83 |
| abstract_inverted_index.bias. | 112 |
| abstract_inverted_index.makes | 67 |
| abstract_inverted_index.plots | 15 |
| abstract_inverted_index.rate. | 126 |
| abstract_inverted_index.shows | 118, 160 |
| abstract_inverted_index.stand | 159 |
| abstract_inverted_index.study | 135 |
| abstract_inverted_index.where | 138 |
| abstract_inverted_index.which | 66, 174 |
| abstract_inverted_index.89.35% | 124 |
| abstract_inverted_index.effect | 140 |
| abstract_inverted_index.errors | 51, 109, 146 |
| abstract_inverted_index.forest | 11, 14, 158 |
| abstract_inverted_index.handle | 105 |
| abstract_inverted_index.method | 2, 28, 95, 121 |
| abstract_inverted_index.metric | 60 |
| abstract_inverted_index.sensor | 154 |
| abstract_inverted_index.signal | 111 |
| abstract_inverted_index.single | 7, 54 |
| abstract_inverted_index.stands | 12, 47 |
| abstract_inverted_index.static | 18 |
| abstract_inverted_index.average | 163 |
| abstract_inverted_index.between | 72 |
| abstract_inverted_index.capture | 24 |
| abstract_inverted_index.correct | 73 |
| abstract_inverted_index.density | 143 |
| abstract_inverted_index.diverse | 45 |
| abstract_inverted_index.dynamic | 22 |
| abstract_inverted_index.linking | 35, 40, 100 |
| abstract_inverted_index.method. | 180 |
| abstract_inverted_index.nearest | 164 |
| abstract_inverted_index.quality | 59 |
| abstract_inverted_index.sensors | 106 |
| abstract_inverted_index.accuracy | 41, 101 |
| abstract_inverted_index.combined | 87 |
| abstract_inverted_index.consists | 29 |
| abstract_inverted_index.diameter | 33, 46 |
| abstract_inverted_index.drifting | 108 |
| abstract_inverted_index.features | 85 |
| abstract_inverted_index.improves | 38 |
| abstract_inverted_index.location | 91 |
| abstract_inverted_index.neighbor | 165 |
| abstract_inverted_index.operates | 97 |
| abstract_inverted_index.position | 50, 145 |
| abstract_inverted_index.possible | 69 |
| abstract_inverted_index.settings | 131 |
| abstract_inverted_index.weighted | 34 |
| abstract_inverted_index.algorithm | 36 |
| abstract_inverted_index.decreased | 167 |
| abstract_inverted_index.detection | 125 |
| abstract_inverted_index.different | 130 |
| abstract_inverted_index.distances | 166 |
| abstract_inverted_index.incorrect | 75 |
| abstract_inverted_index.indicates | 175 |
| abstract_inverted_index.operating | 43 |
| abstract_inverted_index.simulated | 116 |
| abstract_inverted_index.(>99%). | 82 |
| abstract_inverted_index.applicable | 16 |
| abstract_inverted_index.detectors. | 56 |
| abstract_inverted_index.introduced | 65 |
| abstract_inverted_index.presented, | 137 |
| abstract_inverted_index.presented. | 26 |
| abstract_inverted_index.simulation | 134 |
| abstract_inverted_index.statistics | 128 |
| abstract_inverted_index.threshold, | 61 |
| abstract_inverted_index.feasibility | 177 |
| abstract_inverted_index.probability | 81 |
| abstract_inverted_index.discriminate | 71 |
| abstract_inverted_index.simultaneous | 90 |
| abstract_inverted_index.investigated. | 148 |
| abstract_inverted_index.mapping-based | 93 |
| abstract_inverted_index.co-registration | 5, 58, 94 |
| abstract_inverted_index.co-registrations | 78 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.51527599 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |