{"id":"https://openalex.org/W7131220922","doi":"https://doi.org/10.1109/vcip67698.2025.11396858","title":"SimForest: RGBD Instance Segmentation Dataset","display_name":"SimForest: RGBD Instance Segmentation Dataset","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7131220922","doi":"https://doi.org/10.1109/vcip67698.2025.11396858"},"language":null,"primary_location":{"id":"doi:10.1109/vcip67698.2025.11396858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","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/A5055983174","display_name":"Ramana R. Avula","orcid":"https://orcid.org/0000-0001-9672-2689"},"institutions":[{"id":"https://openalex.org/I2800664555","display_name":"RISE Research Institutes of Sweden","ror":"https://ror.org/03nnxqz81","country_code":"SE","type":"other","lineage":["https://openalex.org/I2800664555"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Ramana Reddy Avula","raw_affiliation_strings":["RISE Research Institutes of Sweden,Dependable Transport Systems,Borås,Sweden"],"affiliations":[{"raw_affiliation_string":"RISE Research Institutes of Sweden,Dependable Transport Systems,Borås,Sweden","institution_ids":["https://openalex.org/I2800664555"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124892492","display_name":"Aleksi Narkilahti","orcid":null},"institutions":[{"id":"https://openalex.org/I95220073","display_name":"University of Lapland","ror":"https://ror.org/05jzt8766","country_code":"FI","type":"education","lineage":["https://openalex.org/I95220073"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Aleksi Narkilahti","raw_affiliation_strings":["Lapland University of Applied Sciences,FrostBit Software Lab,Rovaniemi,Finland"],"affiliations":[{"raw_affiliation_string":"Lapland University of Applied Sciences,FrostBit Software Lab,Rovaniemi,Finland","institution_ids":["https://openalex.org/I95220073"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126665364","display_name":"Krzysztof Wo\u0142k","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Krzysztof Wo\u0142k","raw_affiliation_strings":["DAC.Digital, SA,Gdańsk,Poland,80-309"],"affiliations":[{"raw_affiliation_string":"DAC.Digital, SA,Gdańsk,Poland,80-309","institution_ids":["https://openalex.org/I4210110242"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055983174"],"corresponding_institution_ids":["https://openalex.org/I2800664555"],"apc_list":null,"apc_paid":null,"fwci":1.1874,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86637921,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.2669999897480011,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.2669999897480011,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10036","display_name":"Advanced Neural Network Applications","score":0.21160000562667847,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.05939999967813492,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7275999784469604},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5042999982833862},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5013999938964844},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.4853000044822693},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45980000495910645},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4438999891281128},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4007999897003174},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4004000127315521},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3847000002861023}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7967000007629395},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7275999784469604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7073000073432922},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5817000269889832},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45980000495910645},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4438999891281128},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3847000002861023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2694999873638153},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2983008078","wikidata":"https://www.wikidata.org/wiki/Q863495","display_name":"Grid cell","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip67698.2025.11396858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2952122856","https://openalex.org/W4367154333","https://openalex.org/W4399144538","https://openalex.org/W4407929026"],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"perception":[1,175],"in":[2,39,176],"forest":[3,27,115],"environments":[4],"requires":[5],"accurate":[6],"detection":[7,132,149],"and":[8,18,33,85,95,102,109,133,154,178],"segmentation":[9,35,83,134,156],"of":[10,24,123,135,152,159,166],"complex":[11],"natural":[12],"objects":[13],"such":[14,98],"as":[15,99,168],"trees,":[16],"rocks,":[17],"terrain":[19,91],"features.":[20],"However,":[21],"the":[22,121,131,164],"scarcity":[23],"large-scale,":[25],"annotated":[26,75],"datasets,":[28],"especially":[29],"those":[30],"with":[31,76],"depth":[32,80],"instance":[34,82],"labels,":[36],"hinders":[37],"progress":[38],"deploying":[40],"robust":[41],"deep":[42],"learning":[43],"models":[44,140],"for":[45,172],"forestry":[46,63,177],"applications.":[47,182],"In":[48],"this":[49],"paper,":[50],"we":[51,125],"present":[52],"SimForest,":[53,124],"a":[54,61,113,169],"4K-resolution":[55],"synthetic":[56],"RGBD":[57,174],"dataset":[58],"generated":[59],"using":[60,138],"photorealistic":[62],"simulator":[64],"built":[65],"on":[66,142],"Unreal":[67],"Engine":[68],"5.":[69],"SimForest":[70,143,167],"comprises":[71],"5,000":[72],"images,":[73],"each":[74],"aligned":[77],"RGB":[78],"data,":[79],"maps,":[81],"masks,":[84],"detailed":[86],"metadata":[87],"including":[88],"object":[89],"poses,":[90],"depth,":[92],"camera":[93],"parameters,":[94],"environmental":[96],"conditions":[97],"season,":[100],"time,":[101],"cloudiness.":[103],"The":[104,145],"virtual":[105],"scenes":[106],"are":[107],"geo-located":[108],"seasonally":[110],"matched":[111],"to":[112],"real":[114],"near":[116],"Umea,":[117],"Sweden.":[118],"To":[119],"demonstrate":[120],"utility":[122],"conduct":[126],"an":[127],"experimental":[128],"study":[129],"involving":[130],"tree":[136],"trunks":[137],"YOLOv11-based":[139],"trained":[141],"data.":[144],"evaluation":[146],"shows":[147],"strong":[148],"accuracy":[150],"(mAP@50":[151,158],"0.92)":[153],"solid":[155],"performance":[157],"0.74).":[160],"These":[161],"findings":[162],"highlight":[163],"potential":[165],"valuable":[170],"resource":[171],"near-field":[173],"related":[179],"outdoor":[180],"robotics":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-02-25T00:00:00"}