{"id":"https://openalex.org/W7147014590","doi":"https://doi.org/10.48550/arxiv.2603.27101","title":"PRUE: A Practical Recipe for Field Boundary Segmentation at Scale","display_name":"PRUE: A Practical Recipe for Field Boundary Segmentation at Scale","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7147014590","doi":"https://doi.org/10.48550/arxiv.2603.27101"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27101","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.27101","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052728110","display_name":"Gedeon Muhawenayo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Muhawenayo, Gedeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132718802","display_name":"Caleb Robinson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robinson, Caleb","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038402302","display_name":"Subash Khanal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khanal, Subash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006736471","display_name":"Zhanpei Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Zhanpei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035385486","display_name":"Isaac Corley","orcid":"https://orcid.org/0000-0002-9273-7303"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Corley, Isaac","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132658410","display_name":"Alexander Wollam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wollam, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132654612","display_name":"Tianyi Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Tianyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132678613","display_name":"Leonard Strnad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Strnad, Leonard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073729170","display_name":"Ryan Avery","orcid":"https://orcid.org/0000-0001-7392-1474"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Avery, Ryan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089787049","display_name":"Lyndon Estes","orcid":"https://orcid.org/0000-0002-9358-816X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Estes, Lyndon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020096780","display_name":"Ana Mar\u00eda T\u00e1rano","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"T\u00e1rano, Ana M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029557305","display_name":"Nathan Jacobs","orcid":"https://orcid.org/0000-0002-4242-8967"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jacobs, Nathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053180513","display_name":"Hannah Kerner","orcid":"https://orcid.org/0000-0002-3259-7759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kerner, Hannah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5052728110"],"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":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.32899999618530273,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.32899999618530273,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10616","display_name":"Smart Agriculture and AI","score":0.16689999401569366,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant 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"}},{"id":"https://openalex.org/T10757","display_name":"Geographic Information Systems Studies","score":0.08150000125169754,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"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":0.746399998664856},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6793000102043152},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5609999895095825},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.546999990940094},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5309000015258789},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5037999749183655},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.48010000586509705}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.746399998664856},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6793000102043152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6705999970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5976999998092651},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5609999895095825},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.546999990940094},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5037999749183655},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4115999937057495},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3815999925136566},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3815000057220459},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2655999958515167},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27101","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.27101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27101","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.6610211133956909,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large-scale":[0],"maps":[1],"of":[2,36,51,79,123],"field":[3,17,45,142,157],"boundaries":[4],"are":[5,19],"essential":[6],"for":[7,15,43,137,160],"agricultural":[8],"monitoring":[9],"tasks.":[10],"Existing":[11],"deep":[12],"learning":[13],"approaches":[14],"satellite-based":[16],"mapping":[18],"sensitive":[20],"to":[21,102],"illumination,":[22],"spatial":[23],"scale,":[24],"and":[25,38,73,81,98,105,116,125,140,149,155],"changes":[26],"in":[27],"geographic":[28],"location.":[29],"We":[30,56,84,151],"conduct":[31],"the":[32,49,128],"first":[33],"systematic":[34],"evaluation":[35],"segmentation":[37,69,88],"geospatial":[39],"foundation":[40],"models":[41,59,154],"(GFMs)":[42],"global":[44],"boundary":[46,143,158],"delineation":[47,144],"using":[48],"Fields":[50],"The":[52],"World":[53],"(FTW)":[54],"benchmark.":[55],"evaluate":[57],"18":[58],"under":[60,107],"unified":[61],"experimental":[62],"settings,":[63],"showing":[64],"that":[65,90],"a":[66,77,86,92,113,134],"U-Net":[67,93],"semantic":[68],"model":[70,111,146],"outperforms":[71],"instance-based":[72],"GFM":[74],"alternatives":[75],"on":[76,119],"suite":[78],"performance":[80,104],"deployment":[82],"metrics.":[83],"propose":[85],"new":[87],"approach":[89,132],"combines":[91],"backbone,":[94],"composite":[95],"loss":[96],"functions,":[97],"targeted":[99],"data":[100],"augmentations":[101],"enhance":[103],"robustness":[106],"real-world":[108],"conditions.":[109],"Our":[110,131],"achieves":[112],"76\\%":[114],"IoU":[115],"47\\%":[117],"object-F1":[118],"FTW,":[120],"an":[121],"increase":[122],"6\\%":[124],"9\\%":[126],"over":[127],"previous":[129],"baseline.":[130],"provides":[133],"practical":[135],"framework":[136],"reliable,":[138],"scalable,":[139],"reproducible":[141],"across":[145],"design,":[147],"training,":[148],"inference.":[150],"release":[152],"all":[153],"model-derived":[156],"datasets":[159],"five":[161],"countries.":[162]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-04-02T00:00:00"}