{"id":"https://openalex.org/W7105834079","doi":"https://doi.org/10.1109/tgrs.2025.3633779","title":"GAPL-SegNet: Geometry-Aware Prototype Learning for Few-Shot Building Segmentation in Remote Sensing Imagery","display_name":"GAPL-SegNet: Geometry-Aware Prototype Learning for Few-Shot Building Segmentation in Remote Sensing Imagery","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7105834079","doi":"https://doi.org/10.1109/tgrs.2025.3633779"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3633779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3633779","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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":null,"display_name":"Yongtao Deng","orcid":"https://orcid.org/0000-0001-6423-885X"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongtao Deng","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-6423-885X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dajiang Lei","orcid":"https://orcid.org/0000-0002-7482-6417"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dajiang Lei","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-7482-6417","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liping Zhang","orcid":"https://orcid.org/0000-0002-3045-3073"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Zhang","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-3045-3073","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kai Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yidong Peng","orcid":"https://orcid.org/0000-0003-3779-0360"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidong Peng","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0003-3779-0360","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":null,"display_name":"Weisheng Li","orcid":"https://orcid.org/0000-0002-9033-8245"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weisheng Li","raw_affiliation_strings":["School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-9033-8245","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology and Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48639742,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8048999905586243,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8048999905586243,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.08169999718666077,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.023099999874830246,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5906000137329102},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5540000200271606},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5486999750137329},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5212000012397766},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48179998993873596},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.44679999351501465},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.41100001335144043},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4052000045776367},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.4049000144004822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7570000290870667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6305999755859375},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6028000116348267},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48179998993873596},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.44679999351501465},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42500001192092896},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4052000045776367},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.4049000144004822},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.383899986743927},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3633779","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3633779","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.629542350769043,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3453920714","display_name":null,"funder_award_id":"62221005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6096862915","display_name":"\u57fa\u4e8e\u4e09\u5149\u5b50\u8f6f\u7b26\u5408\u7684PET\u53cc\u6838\u7d20\u540c\u65f6\u6210\u50cf\u6280\u672f\u7814\u7a76","funder_award_id":"62071362","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G613398671","display_name":null,"funder_award_id":"62471372","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6288977234","display_name":null,"funder_award_id":"62027901","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6312593824","display_name":null,"funder_award_id":"CYS240413","funder_id":"https://openalex.org/F4320335872","funder_display_name":"Chongqing Graduate Student Research Innovation Project"},{"id":"https://openalex.org/G7760544311","display_name":null,"funder_award_id":"A2023-01","funder_id":"https://openalex.org/F4320322687","funder_display_name":"Chongqing University of Posts and Telecommunications"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322687","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26"},{"id":"https://openalex.org/F4320335872","display_name":"Chongqing Graduate Student Research Innovation Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2560023338","https://openalex.org/W2609402060","https://openalex.org/W2787091153","https://openalex.org/W2908320224","https://openalex.org/W2963078159","https://openalex.org/W2963599420","https://openalex.org/W2963881378","https://openalex.org/W2990230185","https://openalex.org/W3138516171","https://openalex.org/W3164401570","https://openalex.org/W4214573368","https://openalex.org/W4321769975","https://openalex.org/W4364321988","https://openalex.org/W4390874575","https://openalex.org/W4401324241","https://openalex.org/W4402916227","https://openalex.org/W4403209818","https://openalex.org/W4406610935","https://openalex.org/W4409991484","https://openalex.org/W4410226866","https://openalex.org/W4410795999","https://openalex.org/W4412171120","https://openalex.org/W4414856616"],"related_works":[],"abstract_inverted_index":{"Few-shot":[0],"building":[1,20,261],"segmentation":[2],"in":[3,19],"remote":[4],"sensing":[5],"imagery":[6],"remains":[7],"challenging":[8],"due":[9],"to":[10,32,188,199,216,220,276],"limited":[11],"annotated":[12],"data":[13],"and":[14,36,81,125,142,197,256,272],"complex":[15],"geometric":[16,27,70,105,115,123,238,266],"structures":[17],"inherent":[18],"footprints.":[21],"Traditional":[22],"prototype-based":[23],"methods":[24],"ignore":[25],"rich":[26],"priors":[28,239,267],"of":[29,122,237],"buildings,":[30],"leading":[31],"suboptimal":[33],"feature":[34,56,71],"representations":[35],"poor":[37],"generalization":[38],"across":[39,139],"different":[40],"geographic":[41],"regions.":[42],"We":[43],"propose":[44],"GAPL-SegNet,":[45],"a":[46,69,88,108,249],"novel":[47],"architecture":[48],"that":[49,73,93,118,211,233,245],"integrates":[50],"Geometry-Aware":[51],"Prototype":[52],"Learning":[53],"with":[54,113,149,166,194,205,253],"adaptive":[55,110],"aggregation":[57],"for":[58,98],"superior":[59],"few-shot":[60,264],"performance.":[61],"Our":[62],"approach":[63],"introduces":[64],"several":[65],"key":[66],"innovations:":[67],"(1)":[68],"extractor":[72],"captures":[74],"building-specific":[75],"structural":[76],"patterns":[77],"including":[78],"edges,":[79],"corners,":[80],"rectangularity":[82],"through":[83],"dedicated":[84],"detection":[85],"branches;":[86],"(2)":[87],"geometry-aware":[89],"prototype":[90,101],"learning":[91],"framework":[92],"leverages":[94],"spatial":[95,212],"importance":[96],"weighting":[97,117],"more":[99],"discriminative":[100],"construction":[102],"based":[103,161],"on":[104,162],"significance;":[106],"(3)":[107],"progressive":[109],"training":[111,151],"strategy":[112],"dynamic":[114],"loss":[116],"ensures":[119],"effective":[120],"integration":[121],"priors;":[124],"(4)":[126],"systematic":[127],"cross-dataset":[128],"analysis":[129,209,243],"quantifying":[130],"domain":[131,230],"adaptation":[132,226],"challenges.":[133],"Extensive":[134],"experiments":[135],"demonstrate":[136],"strong":[137],"performance":[138],"multiple":[140],"datasets":[141],"scenarios.":[143],"On":[144],"the":[145,173,228,235],"WHU":[146,187,200],"Building":[147],"dataset":[148],"100":[150],"samples,":[152],"GAPL-SegNet":[153],"achieves":[154,190,248],"85.75\u00b10.43%":[155],"IoU":[156,192,203,222],"(95%":[157],"CI:":[158],"[85.22,":[159],"86.29])":[160],"five":[163],"independent":[164],"runs,":[165],"notable":[167],"stability":[168],"(CV":[169],"<":[170],"0.5%),":[171],"outperforming":[172],"best":[174],"baseline":[175],"by":[176],"3.67%":[177],"IoU.":[178],"Cross-dataset":[179],"evaluations":[180],"reveal":[181],"significant":[182],"challenges":[183],"from":[184],"resolution":[185,195,206,213,225],"differences:":[186],"Inria":[189,198],"51.68%":[191],"(41.4%":[193],"alignment)":[196],"reaches":[201],"53.71%":[202],"(33.6%":[204],"alignment).":[207],"Multi-resolution":[208],"demonstrates":[210],"differences":[214],"(0.3m":[215],"1.2m)":[217],"cause":[218],"up":[219],"46%":[221],"degradation,":[223],"identifying":[224],"as":[227],"primary":[229],"shift":[231],"challenge":[232],"exceeds":[234],"scope":[236],"alone.":[240],"Computational":[241],"efficiency":[242,274],"reveals":[244],"our":[246],"method":[247],"favorable":[250],"performance-efficiency":[251],"balance":[252],"22.5M":[254],"parameters":[255],"5.3ms":[257],"inference":[258],"time.":[259],"In":[260],"segmentation\u2019s":[262],"specific":[263],"setting,":[265],"bring":[268],"better":[269],"boundary":[270],"consistency":[271],"deployment":[273],"compared":[275],"general-purpose":[277],"foundation":[278],"models.":[279]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-17T00:00:00"}
