{"id":"https://openalex.org/W4402495070","doi":"https://doi.org/10.3390/rs16183386","title":"Multilevel Geometric Feature Embedding in Transformer Network for ALS Point Cloud Semantic Segmentation","display_name":"Multilevel Geometric Feature Embedding in Transformer Network for ALS Point Cloud Semantic Segmentation","publication_year":2024,"publication_date":"2024-09-12","ids":{"openalex":"https://openalex.org/W4402495070","doi":"https://doi.org/10.3390/rs16183386"},"language":"en","primary_location":{"id":"doi:10.3390/rs16183386","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16183386","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/rs16183386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030763479","display_name":"Zhuanxin Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhuanxin Liang","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China"],"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027377914","display_name":"Xudong Lai","orcid":"https://orcid.org/0000-0003-4611-820X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Lai","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China"],"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030763479"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.01,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.7309991,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"16","issue":"18","first_page":"3386","last_page":"3386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8140275478363037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7775319218635559},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5787390470504761},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5632252097129822},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5626248121261597},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5190335512161255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43115469813346863},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3995272219181061}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8140275478363037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775319218635559},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5787390470504761},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5632252097129822},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5626248121261597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5190335512161255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43115469813346863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3995272219181061},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16183386","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16183386","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:46fbb09f536e4a2e888d85fd6aaa9e7e","is_oa":true,"landing_page_url":"https://doaj.org/article/46fbb09f536e4a2e888d85fd6aaa9e7e","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 18, p 3386 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16183386","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16183386","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1054412453","display_name":null,"funder_award_id":"2023-2-06","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3169549082","display_name":null,"funder_award_id":"L2023G016","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8027055224","display_name":null,"funder_award_id":"42130105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8476665820","display_name":null,"funder_award_id":"2042024kf0029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1973644502","https://openalex.org/W2194775991","https://openalex.org/W2211722331","https://openalex.org/W2339077268","https://openalex.org/W2560609797","https://openalex.org/W2606202972","https://openalex.org/W2624503621","https://openalex.org/W2761129952","https://openalex.org/W2896457183","https://openalex.org/W2948703293","https://openalex.org/W2963231572","https://openalex.org/W2963509914","https://openalex.org/W2964110616","https://openalex.org/W2979750740","https://openalex.org/W2985812748","https://openalex.org/W2990613095","https://openalex.org/W2995307462","https://openalex.org/W3016245793","https://openalex.org/W3034885317","https://openalex.org/W3040815894","https://openalex.org/W3043099088","https://openalex.org/W3111535274","https://openalex.org/W3136610752","https://openalex.org/W3153465022","https://openalex.org/W3157162321","https://openalex.org/W3158097430","https://openalex.org/W3163821474","https://openalex.org/W3196694720","https://openalex.org/W4205323575","https://openalex.org/W4210998390","https://openalex.org/W4214755140","https://openalex.org/W4225759834","https://openalex.org/W4226099359","https://openalex.org/W4281732104","https://openalex.org/W4285411130","https://openalex.org/W4293795076","https://openalex.org/W4306175953","https://openalex.org/W4311080767","https://openalex.org/W4380303698","https://openalex.org/W4383900005","https://openalex.org/W4387971736","https://openalex.org/W6640300118","https://openalex.org/W6763367864","https://openalex.org/W6839446344","https://openalex.org/W6842767861"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W3108295644","https://openalex.org/W1578717197","https://openalex.org/W2626737336","https://openalex.org/W4399442168","https://openalex.org/W2114282491"],"abstract_inverted_index":{"Effective":[0],"semantic":[1,68],"segmentation":[2],"of":[3,14,98,128,174],"Airborne":[4],"Laser":[5],"Scanning":[6],"(ALS)":[7],"point":[8,19,58,66,176],"clouds":[9,59],"is":[10],"a":[11,39,109,117,149],"crucial":[12],"field":[13,97],"study":[15],"and":[16,60,85,102,200,206,209],"influences":[17],"subsequent":[18],"cloud":[20,67],"application":[21],"tasks.":[22],"Transformer":[23],"networks":[24],"have":[25],"made":[26],"significant":[27],"progress":[28],"in":[29,64,133,177],"2D/3D":[30],"computer":[31],"vision":[32],"tasks,":[33],"exhibiting":[34],"superior":[35,198],"performance.":[36],"We":[37],"propose":[38,116],"multilevel":[40,154],"geometric":[41,76,104],"feature":[42,160],"embedding":[43],"transformer":[44,62,90],"network":[45,168,196],"(MGFE-T),":[46],"which":[47],"aims":[48],"to":[49,124,156],"fully":[50],"utilize":[51],"the":[52,71,75,89,95,99,103,126,138,158,171,194,204,213],"three-dimensional":[53],"structural":[54],"information":[55],"carried":[56],"by":[57],"enhance":[61],"performance":[63],"ALS":[65],"segmentation.":[69],"In":[70,137],"encoding":[72],"stage,":[73,140],"compute":[74],"features":[77],"surrounding":[78],"tee":[79],"sampling":[80],"points":[81],"at":[82,112,145,163],"each":[83,113,146,175],"layer":[84],"embed":[86],"them":[87],"into":[88,148],"workflow.":[91],"To":[92],"ensure":[93],"that":[94,193],"receptive":[96],"self-attention":[100],"mechanism":[101],"computation":[105],"domain":[106,135],"can":[107,169],"maintain":[108],"consistent":[110],"scale":[111],"layer,":[114],"we":[115,141],"fixed-radius":[118],"dilated":[119],"KNN":[120,130],"(FR-DKNN)":[121],"search":[122,131],"method":[123],"address":[125],"limitation":[127],"traditional":[129],"methods":[132],"considering":[134],"radius.":[136],"decoding":[139],"aggregate":[142],"prediction":[143],"deviations":[144],"level":[147],"unified":[150],"loss":[151],"value,":[152],"enabling":[153],"supervision":[155],"improve":[157],"network\u2019s":[159],"learning":[161],"ability":[162],"different":[164],"levels.":[165],"The":[166,190],"MGFE-T":[167,195],"predict":[170],"class":[172],"label":[173],"an":[178],"end-to-end":[179],"manner.":[180],"Experiments":[181],"were":[182],"conducted":[183],"on":[184,203,212],"three":[185],"widely":[186],"used":[187],"benchmark":[188],"datasets.":[189],"results":[191],"indicate":[192],"achieves":[197],"OA":[199],"mF1":[201],"scores":[202],"LASDU":[205],"DFC2019":[207],"datasets":[208],"performs":[210],"well":[211],"ISPRS":[214],"dataset":[215],"with":[216],"imbalanced":[217],"classes.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}