{"id":"https://openalex.org/W4312518244","doi":"https://doi.org/10.1109/geoinformatics57846.2022.9963816","title":"Research on the extraction method of impervious surface in Nanjing based on random forest classification","display_name":"Research on the extraction method of impervious surface in Nanjing based on random forest classification","publication_year":2022,"publication_date":"2022-08-15","ids":{"openalex":"https://openalex.org/W4312518244","doi":"https://doi.org/10.1109/geoinformatics57846.2022.9963816"},"language":"en","primary_location":{"id":"doi:10.1109/geoinformatics57846.2022.9963816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics57846.2022.9963816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 29th International Conference on Geoinformatics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101994157","display_name":"Ying Xu","orcid":"https://orcid.org/0000-0003-0709-3417"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Xu","raw_affiliation_strings":["School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016764541","display_name":"Chang Jiang","orcid":"https://orcid.org/0000-0003-0406-8523"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Jiang","raw_affiliation_strings":["School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102499599","display_name":"LI Xian-lei","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianlei Li","raw_affiliation_strings":["School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051358574","display_name":"Enze Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enze Ji","raw_affiliation_strings":["School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033720761","display_name":"Siqin Ban","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siqin Ban","raw_affiliation_strings":["School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101994157"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.1703,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.44802449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10692","display_name":"Urban Green Space and Health","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/impervious-surface","display_name":"Impervious surface","score":0.9334397912025452},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6887603998184204},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6623303890228271},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6148093938827515},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5046747922897339},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4965274930000305},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44520893692970276},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.44371411204338074},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4344889521598816},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41944313049316406},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.410489946603775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3841176927089691},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17190080881118774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15289410948753357}],"concepts":[{"id":"https://openalex.org/C2668921","wikidata":"https://www.wikidata.org/wiki/Q1434713","display_name":"Impervious surface","level":2,"score":0.9334397912025452},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6887603998184204},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6623303890228271},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6148093938827515},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5046747922897339},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4965274930000305},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44520893692970276},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.44371411204338074},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4344889521598816},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41944313049316406},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.410489946603775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3841176927089691},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17190080881118774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15289410948753357},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/geoinformatics57846.2022.9963816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics57846.2022.9963816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 29th International Conference on Geoinformatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1964217023","https://openalex.org/W2026332487","https://openalex.org/W2038083647","https://openalex.org/W2054116204","https://openalex.org/W2067885219","https://openalex.org/W2147406638","https://openalex.org/W2148100764","https://openalex.org/W2556950716","https://openalex.org/W2578950705","https://openalex.org/W6681845879"],"related_works":["https://openalex.org/W2738109983","https://openalex.org/W2364341326","https://openalex.org/W2757433404","https://openalex.org/W4254235682","https://openalex.org/W2054563345","https://openalex.org/W2520989432","https://openalex.org/W3132993209","https://openalex.org/W2159637219","https://openalex.org/W2271813916","https://openalex.org/W2024488612"],"abstract_inverted_index":{"The":[0,21,75,94],"distribution":[1],"of":[2,17,24,46,61,83,96,116],"urban":[3,19,62,123],"impervious":[4,26,63,124],"surfaces":[5,27,64],"is":[6,100,146],"usually":[7],"recognized":[8],"as":[9],"a":[10,56],"significant":[11],"environmental":[12],"and":[13,38,71,80,89,109,114,141],"an":[14],"essential":[15],"indicator":[16],"various":[18],"problems.":[20],"typical":[22],"methods":[23,49],"extracting":[25],"are":[28,85],"Linear":[29],"Spectral":[30],"Mixture":[31],"Analysis":[32,69],"Method":[33,36,42],"(LSMA),":[34],"Exponential":[35],"(EM),":[37],"Maximum":[39],"Likelihood":[40],"Classification":[41],"(MLC).":[43],"However,":[44],"all":[45],"the":[47,91,103,117,122,129,133,142],"above":[48],"have":[50],"their":[51],"drawbacks.":[52],"Therefore,":[53],"we":[54],"propose":[55],"new":[57,98],"method":[58,99],"for":[59],"extraction":[60],"based":[65],"on":[66],"Principal":[67],"Component":[68],"(PCA)":[70],"Random":[72],"Forest":[73],"(RF).":[74],"synthetic":[76],"aperture":[77],"radar":[78],"data":[79,82],"hyperspectral":[81],"Nanjing":[84],"employed":[86],"to":[87,111,120],"test":[88],"validate":[90],"proposed":[92,118],"methodology.":[93],"result":[95],"this":[97],"compared":[101],"with":[102],"results":[104],"obtained":[105],"by":[106],"LSMA,":[107],"EM,":[108],"MLC":[110],"evaluate":[112],"feasibility":[113],"effectiveness":[115],"methodology":[119],"extract":[121],"surface.":[125],"Experiments":[126],"show":[127],"that":[128],"Kappa":[130],"coefficient":[131],"in":[132],"optimal":[134],"random":[135],"forest":[136],"model":[137],"can":[138],"reach":[139],"0.9744":[140],"overall":[143],"classification":[144],"accuracy":[145],"99.6402%,":[147],"which":[148],"has":[149],"obvious":[150],"advantages":[151],"over":[152],"other":[153],"methods.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}