A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE Article Swipe
Haiyan Gu
,
H. T. Li
,
Zehao Liu
,
Chongjian Shao
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017
Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017
- https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.pdf
- OA Status
- diamond
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2756459432
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2756459432Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017Digital Object Identifier
- Title
-
A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGEWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-09-13Full publication date if available
- Authors
-
Haiyan Gu, H. T. Li, Zehao Liu, Chongjian ShaoList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017Publisher landing page
- PDF URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.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://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.pdfDirect OA link when available
- Concepts
-
Computer science, Decision tree, Machine learning, Set (abstract data type), Artificial intelligence, Data mining, Land cover, Rule-based system, Decision rule, Cover (algebra), Workflow, Random forest, Land use, Engineering, Database, Programming language, Civil engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2756459432 |
|---|---|
| doi | https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017 |
| ids.doi | https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017 |
| ids.mag | 2756459432 |
| ids.openalex | https://openalex.org/W2756459432 |
| fwci | 0.0 |
| type | article |
| title | A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE |
| biblio.issue | |
| biblio.volume | XLII-2/W7 |
| biblio.last_page | 732 |
| biblio.first_page | 729 |
| topics[0].id | https://openalex.org/T10689 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9912999868392944 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2214 |
| topics[0].subfield.display_name | Media Technology |
| topics[0].display_name | Remote-Sensing Image Classification |
| topics[1].id | https://openalex.org/T10111 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9853000044822693 |
| 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 | Remote Sensing in Agriculture |
| topics[2].id | https://openalex.org/T13058 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9851999878883362 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2308 |
| topics[2].subfield.display_name | Management, Monitoring, Policy and Law |
| topics[2].display_name | Soil and Land Suitability Analysis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7172614336013794 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C84525736 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6040741801261902 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[1].display_name | Decision tree |
| concepts[2].id | https://openalex.org/C119857082 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5895718932151794 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[2].display_name | Machine learning |
| concepts[3].id | https://openalex.org/C177264268 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5813149213790894 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[3].display_name | Set (abstract data type) |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5773757696151733 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C124101348 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5691561102867126 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[5].display_name | Data mining |
| concepts[6].id | https://openalex.org/C2780648208 |
| concepts[6].level | 3 |
| concepts[6].score | 0.55010986328125 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3001793 |
| concepts[6].display_name | Land cover |
| concepts[7].id | https://openalex.org/C149271511 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5149458050727844 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1417149 |
| concepts[7].display_name | Rule-based system |
| concepts[8].id | https://openalex.org/C84839998 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4963222146034241 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5249245 |
| concepts[8].display_name | Decision rule |
| concepts[9].id | https://openalex.org/C2780428219 |
| concepts[9].level | 2 |
| concepts[9].score | 0.43895524740219116 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q16952335 |
| concepts[9].display_name | Cover (algebra) |
| concepts[10].id | https://openalex.org/C177212765 |
| concepts[10].level | 2 |
| concepts[10].score | 0.426810085773468 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q627335 |
| concepts[10].display_name | Workflow |
| concepts[11].id | https://openalex.org/C169258074 |
| concepts[11].level | 2 |
| concepts[11].score | 0.42597901821136475 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[11].display_name | Random forest |
| concepts[12].id | https://openalex.org/C4792198 |
| concepts[12].level | 2 |
| concepts[12].score | 0.30999451875686646 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1165944 |
| concepts[12].display_name | Land use |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.1097661554813385 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C77088390 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0774318277835846 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[14].display_name | Database |
| concepts[15].id | https://openalex.org/C199360897 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[15].display_name | Programming language |
| concepts[16].id | https://openalex.org/C147176958 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q77590 |
| concepts[16].display_name | Civil engineering |
| concepts[17].id | https://openalex.org/C78519656 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[17].display_name | Mechanical engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7172614336013794 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/decision-tree |
| keywords[1].score | 0.6040741801261902 |
| keywords[1].display_name | Decision tree |
| keywords[2].id | https://openalex.org/keywords/machine-learning |
| keywords[2].score | 0.5895718932151794 |
| keywords[2].display_name | Machine learning |
| keywords[3].id | https://openalex.org/keywords/set |
| keywords[3].score | 0.5813149213790894 |
| keywords[3].display_name | Set (abstract data type) |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5773757696151733 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/data-mining |
| keywords[5].score | 0.5691561102867126 |
| keywords[5].display_name | Data mining |
| keywords[6].id | https://openalex.org/keywords/land-cover |
| keywords[6].score | 0.55010986328125 |
| keywords[6].display_name | Land cover |
| keywords[7].id | https://openalex.org/keywords/rule-based-system |
| keywords[7].score | 0.5149458050727844 |
| keywords[7].display_name | Rule-based system |
| keywords[8].id | https://openalex.org/keywords/decision-rule |
| keywords[8].score | 0.4963222146034241 |
| keywords[8].display_name | Decision rule |
| keywords[9].id | https://openalex.org/keywords/cover |
| keywords[9].score | 0.43895524740219116 |
| keywords[9].display_name | Cover (algebra) |
| keywords[10].id | https://openalex.org/keywords/workflow |
| keywords[10].score | 0.426810085773468 |
| keywords[10].display_name | Workflow |
| keywords[11].id | https://openalex.org/keywords/random-forest |
| keywords[11].score | 0.42597901821136475 |
| keywords[11].display_name | Random forest |
| keywords[12].id | https://openalex.org/keywords/land-use |
| keywords[12].score | 0.30999451875686646 |
| keywords[12].display_name | Land use |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.1097661554813385 |
| keywords[13].display_name | Engineering |
| keywords[14].id | https://openalex.org/keywords/database |
| keywords[14].score | 0.0774318277835846 |
| keywords[14].display_name | Database |
| language | en |
| locations[0].id | doi:10.5194/isprs-archives-xlii-2-w7-729-2017 |
| 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://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.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-xlii-2-w7-729-2017 |
| locations[1].id | pmh:oai:doaj.org/article:1128e56caa814e1a9957ef6d0353f9bf |
| 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 XLII-2-W7, Pp 729-732 (2017) |
| locations[1].landing_page_url | https://doaj.org/article/1128e56caa814e1a9957ef6d0353f9bf |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5101633208 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7477-4736 |
| authorships[0].author.display_name | Haiyan Gu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210114963 |
| authorships[0].affiliations[0].raw_affiliation_string | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210114963 |
| authorships[0].institutions[0].ror | https://ror.org/02j693n47 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210114963 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Chinese Academy of Surveying and Mapping |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | H. Y. Gu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[1].author.id | https://openalex.org/A5046281951 |
| authorships[1].author.orcid | https://orcid.org/0009-0009-0426-1877 |
| authorships[1].author.display_name | H. T. Li |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210114963 |
| authorships[1].affiliations[0].raw_affiliation_string | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210114963 |
| authorships[1].institutions[0].ror | https://ror.org/02j693n47 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210114963 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Chinese Academy of Surveying and Mapping |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | H. T. Li |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[2].author.id | https://openalex.org/A5022367486 |
| authorships[2].author.orcid | https://orcid.org/0009-0003-0520-8134 |
| authorships[2].author.display_name | Zehao Liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210114963 |
| authorships[2].affiliations[0].raw_affiliation_string | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210114963 |
| authorships[2].institutions[0].ror | https://ror.org/02j693n47 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210114963 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Chinese Academy of Surveying and Mapping |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Z. Y. Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[3].author.id | https://openalex.org/A5008165399 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chongjian Shao |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210114963 |
| authorships[3].affiliations[0].raw_affiliation_string | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210114963 |
| authorships[3].institutions[0].ror | https://ror.org/02j693n47 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210114963 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Chinese Academy of Surveying and Mapping |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | C. Y. Shao |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10689 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9912999868392944 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2214 |
| primary_topic.subfield.display_name | Media Technology |
| primary_topic.display_name | Remote-Sensing Image Classification |
| related_works | https://openalex.org/W4366990902, https://openalex.org/W4317732970, https://openalex.org/W4388550696, https://openalex.org/W4313289487, https://openalex.org/W4321636153, https://openalex.org/W1586045918, https://openalex.org/W1536574209, https://openalex.org/W2023290140, https://openalex.org/W2170530489, https://openalex.org/W1520927778 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5194/isprs-archives-xlii-2-w7-729-2017 |
| 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://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.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-xlii-2-w7-729-2017 |
| primary_location.id | doi:10.5194/isprs-archives-xlii-2-w7-729-2017 |
| 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://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/729/2017/isprs-archives-XLII-2-W7-729-2017.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-xlii-2-w7-729-2017 |
| publication_date | 2017-09-13 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2064125725, https://openalex.org/W1995085431, https://openalex.org/W2541453841, https://openalex.org/W1996482635, https://openalex.org/W2034489756, https://openalex.org/W2911964244, https://openalex.org/W2077312680 |
| referenced_works_count | 7 |
| abstract_inverted_index.A | 112 |
| abstract_inverted_index.a | 42, 50 |
| abstract_inverted_index.We | 162 |
| abstract_inverted_index.an | 26, 119 |
| abstract_inverted_index.in | 34, 159 |
| abstract_inverted_index.is | 4, 31, 38, 71, 89, 122, 136, 157 |
| abstract_inverted_index.it | 37 |
| abstract_inverted_index.of | 19, 68, 86, 94, 102, 109 |
| abstract_inverted_index.on | 25, 60, 99, 125, 141 |
| abstract_inverted_index.to | 12, 72, 90, 150 |
| abstract_inverted_index.we | 145 |
| abstract_inverted_index.DSM | 166 |
| abstract_inverted_index.The | 17, 66, 84, 154, 171 |
| abstract_inverted_index.and | 14, 21, 40, 63, 105, 128, 138, 167, 186 |
| abstract_inverted_index.are | 23, 188 |
| abstract_inverted_index.for | 6, 55, 181, 191 |
| abstract_inverted_index.has | 41, 47, 115 |
| abstract_inverted_index.its | 152 |
| abstract_inverted_index.put | 48 |
| abstract_inverted_index.set | 3, 52, 180 |
| abstract_inverted_index.the | 80, 92, 107, 133, 164, 175 |
| abstract_inverted_index.use | 67, 85, 146 |
| abstract_inverted_index.CART | 129 |
| abstract_inverted_index.Land | 7, 56, 182 |
| abstract_inverted_index.TOP, | 165 |
| abstract_inverted_index.This | 45 |
| abstract_inverted_index.been | 116 |
| abstract_inverted_index.land | 193 |
| abstract_inverted_index.poor | 43 |
| abstract_inverted_index.rule | 2, 51, 74, 110, 121, 135, 179 |
| abstract_inverted_index.sets | 75 |
| abstract_inverted_index.show | 173 |
| abstract_inverted_index.site | 156 |
| abstract_inverted_index.test | 155 |
| abstract_inverted_index.that | 30, 174 |
| abstract_inverted_index.will | 78 |
| abstract_inverted_index.City. | 161 |
| abstract_inverted_index.Cover | 8, 183 |
| abstract_inverted_index.based | 24, 59, 124, 140 |
| abstract_inverted_index.build | 73 |
| abstract_inverted_index.built | 123 |
| abstract_inverted_index.could | 177 |
| abstract_inverted_index.cover | 57, 194 |
| abstract_inverted_index.data. | 170 |
| abstract_inverted_index.human | 61, 87, 142 |
| abstract_inverted_index.often | 32 |
| abstract_inverted_index.prior | 103 |
| abstract_inverted_index.sets. | 111 |
| abstract_inverted_index.solve | 91 |
| abstract_inverted_index.study | 46 |
| abstract_inverted_index.there | 187 |
| abstract_inverted_index.tree. | 131 |
| abstract_inverted_index.truth | 169 |
| abstract_inverted_index.usage | 101 |
| abstract_inverted_index.where | 144 |
| abstract_inverted_index.which | 10, 77 |
| abstract_inverted_index.Forest | 127 |
| abstract_inverted_index.Random | 126 |
| abstract_inverted_index.ground | 168 |
| abstract_inverted_index.method | 54, 98, 176 |
| abstract_inverted_index.refers | 11 |
| abstract_inverted_index.rules. | 16 |
| abstract_inverted_index.static | 189 |
| abstract_inverted_index.GEOBIA, | 35 |
| abstract_inverted_index.Potsdam | 160 |
| abstract_inverted_index.forward | 49 |
| abstract_inverted_index.improve | 106 |
| abstract_inverted_index.initial | 120, 134 |
| abstract_inverted_index.located | 158 |
| abstract_inverted_index.machine | 64, 69, 96 |
| abstract_inverted_index.results | 172 |
| abstract_inverted_index.analyzed | 137 |
| abstract_inverted_index.approach | 29 |
| abstract_inverted_index.building | 53 |
| abstract_inverted_index.classes. | 195 |
| abstract_inverted_index.decision | 15, 22, 130 |
| abstract_inverted_index.existing | 95 |
| abstract_inverted_index.features | 13, 20, 190 |
| abstract_inverted_index.firstly, | 118 |
| abstract_inverted_index.however, | 36 |
| abstract_inverted_index.interval | 149 |
| abstract_inverted_index.learning | 70, 97 |
| abstract_inverted_index.overcome | 79 |
| abstract_inverted_index.two-step | 113 |
| abstract_inverted_index.utilised | 163 |
| abstract_inverted_index.utilized | 33 |
| abstract_inverted_index.workflow | 114 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Secondly, | 132 |
| abstract_inverted_index.approach. | 83 |
| abstract_inverted_index.determine | 151, 178 |
| abstract_inverted_index.different | 192 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.iterative | 27, 81 |
| abstract_inverted_index.knowledge | 62, 88 |
| abstract_inverted_index.learning. | 65 |
| abstract_inverted_index.selection | 18 |
| abstract_inverted_index.validated | 139 |
| abstract_inverted_index.confidence | 148 |
| abstract_inverted_index.knowledge, | 104, 143 |
| abstract_inverted_index.threshold. | 153 |
| abstract_inverted_index.effectively | 76 |
| abstract_inverted_index.introduced, | 117 |
| abstract_inverted_index.statistical | 147 |
| abstract_inverted_index.versatility | 108 |
| abstract_inverted_index.insufficient | 100 |
| abstract_inverted_index.shortcomings | 93 |
| abstract_inverted_index.versatility. | 44 |
| abstract_inverted_index.Classification | 1 |
| abstract_inverted_index.classification | 58, 184 |
| abstract_inverted_index.time-consuming | 39 |
| abstract_inverted_index.classification, | 9 |
| abstract_inverted_index.trial-and-error | 28, 82 |
| abstract_inverted_index.semi-automatically, | 185 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.18173346 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |