{"id":"https://openalex.org/W6907270588","doi":"https://doi.org/10.21227/18zd-7a87","title":"ElecsDataset: a 3D semantic segmentation dataset for substation environments","display_name":"ElecsDataset: a 3D semantic segmentation dataset for substation environments","publication_year":2025,"publication_date":"2025-03-28","ids":{"openalex":"https://openalex.org/W6907270588","doi":"https://doi.org/10.21227/18zd-7a87"},"language":"en","primary_location":{"id":"doi:10.21227/18zd-7a87","is_oa":true,"landing_page_url":"https://doi.org/10.21227/18zd-7a87","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/18zd-7a87","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xia, Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xia, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T14423","display_name":"Military Technology and Strategies","score":0.0210999995470047,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14423","display_name":"Military Technology and Strategies","score":0.0210999995470047,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13154","display_name":"Legal and Regulatory Analysis","score":0.013100000098347664,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14235","display_name":"Linguistic, Cultural, and Literary Studies","score":0.01080000028014183,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6628999710083008},{"id":"https://openalex.org/keywords/economic-shortage","display_name":"Economic shortage","score":0.49880000948905945},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42089998722076416},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41600000858306885},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.40529999136924744},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.3716999888420105},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.3716000020503998},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.36309999227523804}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627999782562256},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6628999710083008},{"id":"https://openalex.org/C194051981","wikidata":"https://www.wikidata.org/wiki/Q1337691","display_name":"Economic shortage","level":3,"score":0.49880000948905945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46149998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45809999108314514},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.321399986743927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30869999527931213},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.26190000772476196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/18zd-7a87","is_oa":true,"landing_page_url":"https://doi.org/10.21227/18zd-7a87","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/18zd-7a87","is_oa":true,"landing_page_url":"https://doi.org/10.21227/18zd-7a87","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4902850091457367,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"ElecsDataset":[0],"is":[1,57,74,92,129],"a":[2],"specialized":[3],"3D":[4,25],"semantic":[5,26],"segmentation":[6],"dataset":[7,29,56,91,128],"designed":[8],"for":[9,131],"substation":[10,24,116],"environments.":[11],"It":[12,44],"addresses":[13],"the":[14,21,77,81,86,90,104],"shortage":[15],"of":[16,23,46,52],"domain-specific":[17],"annotated":[18,33],"data":[19,47],"in":[20],"field":[22],"segmentation.":[27],"This":[28,125],"offers":[30],"high-resolution,":[31],"meticulously":[32],"point":[34],"clouds":[35],"that":[36],"capture":[37],"complex":[38],"equipment":[39,117],"structures":[40],"and":[41,110,122,137],"real-world":[42],"occlusions.":[43],"consists":[45],"collected":[48],"from":[49],"three":[50],"substations":[51],"varying":[53],"scales.":[54,124],"The":[55],"systematically":[58],"partitioned":[59],"into":[60,94,119],"6":[61],"distinct":[62],"spatial":[63],"regions":[64],"with":[65,133],"heterogeneous":[66],"dimensions.":[67],"Following":[68],"standardized":[69],"evaluation":[70],"protocols,":[71],"Area":[72],"5":[73],"designated":[75],"as":[76,113,115],"testing":[78],"subset,":[79],"while":[80],"remaining":[82],"five":[83],"areas":[84],"form":[85],"training":[87],"subset.":[88],"Moreover,":[89],"divided":[93],"10":[95],"categories,":[96],"including":[97],"non-equipment":[98],"entities":[99],"like":[100],"busbars,":[101],"electrical":[102],"wires,":[103],"ground,":[105],"buildings,":[106],"overhead":[107],"structures,":[108],"walls,":[109],"miscellaneous":[111],"objects,":[112],"well":[114],"categorized":[118],"small,":[120],"medium,":[121],"large":[123],"comprehensive":[126],"benchmark":[127],"optimized":[130],"compatibility":[132],"deep":[134],"neural":[135],"architectures":[136],"maintains":[138],"computational":[139],"efficiency":[140],"during":[141],"model":[142],"training.":[143]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}