{"id":"https://openalex.org/W3195248159","doi":"https://doi.org/10.1137/1.9781611976700.29","title":"Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy","display_name":"Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3195248159","doi":"https://doi.org/10.1137/1.9781611976700.29","mag":"3195248159"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611976700.29","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976700.29","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.29","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.29","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070903469","display_name":"Wenchong He","orcid":"https://orcid.org/0000-0001-8115-1115"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenchong He","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, 35487"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, 35487","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034746991","display_name":"Arpan Man Sainju","orcid":"https://orcid.org/0000-0001-5668-194X"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arpan Man Sainju","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, 35487"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, 35487","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021681759","display_name":"Zhe Jiang","orcid":"https://orcid.org/0000-0002-3576-6976"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Jiang","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, 35487"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, 35487","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070582509","display_name":"Da Yan","orcid":"https://orcid.org/0000-0002-4653-0408"},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Yan","raw_affiliation_strings":["The University of Alabama at Birmingham, Birmingham, AL, 35294"],"affiliations":[{"raw_affiliation_string":"The University of Alabama at Birmingham, Birmingham, AL, 35294","institution_ids":["https://openalex.org/I32389192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070903469"],"corresponding_institution_ids":["https://openalex.org/I17301866"],"apc_list":null,"apc_paid":null,"fwci":4.732,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.95209581,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"253","last_page":"261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9901000261306763,"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/T10889","display_name":"Soil erosion and sediment transport","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5892118811607361},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.588523805141449},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5504691004753113},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5452904105186462},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5412167906761169},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5153338313102722},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5153069496154785},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.4792216420173645},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.473875492811203},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45011553168296814},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4367656111717224},{"id":"https://openalex.org/keywords/elevation","display_name":"Elevation (ballistics)","score":0.4112577438354492},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.34034815430641174},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3270973265171051},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.1835923194885254}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5892118811607361},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.588523805141449},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5504691004753113},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5452904105186462},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5412167906761169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5153338313102722},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5153069496154785},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.4792216420173645},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.473875492811203},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45011553168296814},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4367656111717224},{"id":"https://openalex.org/C37054046","wikidata":"https://www.wikidata.org/wiki/Q641888","display_name":"Elevation (ballistics)","level":2,"score":0.4112577438354492},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.34034815430641174},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3270973265171051},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.1835923194885254},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/1.9781611976700.29","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976700.29","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.29","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1137/1.9781611976700.29","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976700.29","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976700.29","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1487762408","display_name":null,"funder_award_id":"1951974","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4823412530","display_name":null,"funder_award_id":"IIS-1850546, IIS-2008973, CNS-1951974","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5299740013","display_name":null,"funder_award_id":"IIS-2008973","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6959102758","display_name":null,"funder_award_id":"CNS-1951974","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G78672691","display_name":"III: Small: Spatial Deep Learning from Imperfect Volunteered Geographic Information","funder_award_id":"2008973","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7891835445","display_name":null,"funder_award_id":"1850546","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8479954819","display_name":null,"funder_award_id":"IIS-1850546","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308614","display_name":"University Corporation for Atmospheric Research","ror":"https://ror.org/04zhhyn23"},{"id":"https://openalex.org/F4320332181","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3195248159.pdf","grobid_xml":"https://content.openalex.org/works/W3195248159.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1526753291","https://openalex.org/W1745334888","https://openalex.org/W1850843018","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1977387474","https://openalex.org/W1979102712","https://openalex.org/W2001088180","https://openalex.org/W2006108538","https://openalex.org/W2023808821","https://openalex.org/W2111258217","https://openalex.org/W2126762337","https://openalex.org/W2131006320","https://openalex.org/W2135957668","https://openalex.org/W2141460641","https://openalex.org/W2468907370","https://openalex.org/W2485522583","https://openalex.org/W2604455318","https://openalex.org/W2630837129","https://openalex.org/W2772628703","https://openalex.org/W2809370672","https://openalex.org/W2809418595","https://openalex.org/W2811124557","https://openalex.org/W2904900486","https://openalex.org/W2905224888","https://openalex.org/W2934379707","https://openalex.org/W2949577571","https://openalex.org/W2951659295","https://openalex.org/W2962996477","https://openalex.org/W2963270775","https://openalex.org/W2963358464","https://openalex.org/W2963896186","https://openalex.org/W2964321699","https://openalex.org/W3008534106","https://openalex.org/W3036384654","https://openalex.org/W3082726537","https://openalex.org/W3101553402","https://openalex.org/W3105136071","https://openalex.org/W3132455321","https://openalex.org/W3138447845","https://openalex.org/W3140579943","https://openalex.org/W4210257598","https://openalex.org/W4229511220","https://openalex.org/W4308909683","https://openalex.org/W4394671248"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Given":[0],"a":[1,10,123,135,141,156],"3D":[2],"surface":[3,25,40,68,93,131,147,168],"defined":[4],"by":[5,134],"an":[6],"elevation":[7,151],"function":[8],"on":[9,35,91,112,122,161,178],"2D":[11],"grid":[12],"as":[13,15],"well":[14],"non-spatial":[16,37],"features":[17,38],"observed":[18],"at":[19,149,171],"each":[20],"pixel,":[21],"the":[22,61,71,82,144,162],"problem":[23,43],"of":[24,64,75,146],"segmentation":[26,103],"aims":[27],"to":[28,109,117,129,166],"classify":[29],"pixels":[30],"into":[31],"contiguous":[32],"classes":[33],"based":[34,90,160,177],"both":[36],"and":[39,51,79,114],"topology.":[41],"The":[42],"has":[44],"important":[45],"applications":[46],"in":[47,70,86,190],"hydrology,":[48],"planetary":[49],"science,":[50],"biochemistry":[52],"but":[53],"is":[54,140],"uniquely":[55],"challenging":[56],"for":[57,101],"several":[58,187],"reasons.":[59],"First,":[60],"spatial":[62,77,88,173],"extent":[63],"class":[65],"segments":[66],"follows":[67],"contours":[69,148],"topological":[72,83,132,169],"space,":[73],"regardless":[74],"their":[76,110],"shapes":[78],"directions.":[80],"Second,":[81],"structure":[84,133,170],"exists":[85],"multiple":[87],"scales":[89],"different":[92,150,172],"resolutions.":[94],"Existing":[95],"widely":[96],"successful":[97],"deep":[98],"learning":[99],"models":[100],"image":[102],"are":[104],"often":[105],"not":[106],"applicable":[107],"due":[108],"reliance":[111],"convolution":[113],"pooling":[115],"operations":[116],"learn":[118],"regular":[119],"structural":[120],"patterns":[121],"grid.":[124],"In":[125],"contrast,":[126],"we":[127],"propose":[128],"represent":[130],"contour":[136,163],"tree":[137,164],"skeleton,":[138],"which":[139],"polytree":[142],"capturing":[143],"evolution":[145],"levels.":[152],"We":[153],"further":[154],"design":[155],"graph":[157],"neural":[158],"network":[159],"hierarchy":[165],"model":[167,185],"scales.":[174],"Experimental":[175],"evaluations":[176],"real-world":[179],"hydrological":[180],"datasets":[181],"show":[182],"that":[183],"our":[184],"outperforms":[186],"baseline":[188],"methods":[189],"classification":[191],"accuracy.":[192]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
