{"id":"https://openalex.org/W4206194838","doi":"https://doi.org/10.3390/rs14030476","title":"Transformer for Tree Counting in Aerial Images","display_name":"Transformer for Tree Counting in Aerial Images","publication_year":2022,"publication_date":"2022-01-20","ids":{"openalex":"https://openalex.org/W4206194838","doi":"https://doi.org/10.3390/rs14030476"},"language":"en","primary_location":{"id":"doi:10.3390/rs14030476","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14030476","pdf_url":"https://www.mdpi.com/2072-4292/14/3/476/pdf?version=1642667263","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/3/476/pdf?version=1642667263","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102992722","display_name":"Guang Chen","orcid":"https://orcid.org/0000-0003-1487-8044"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guang Chen","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65211, USA"],"raw_orcid":"https://orcid.org/0000-0003-1487-8044","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65211, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057883374","display_name":"Yi Shang","orcid":"https://orcid.org/0000-0001-7771-4034"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yi Shang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65211, USA"],"raw_orcid":"https://orcid.org/0000-0001-7771-4034","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science (EECS), University of Missouri, Columbia, MO 65211, USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057883374"],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":2.7588,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.90628474,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"14","issue":"3","first_page":"476","last_page":"476"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9994999766349792,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/computer-science","display_name":"Computer science","score":0.8052483797073364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6799092888832092},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.56351637840271},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5493277907371521},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5180023312568665},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"Aerial imagery","score":0.5024371147155762},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4867801368236542},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4804810583591461},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4283328652381897},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.15123286843299866},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1076536476612091},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09902924299240112}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052483797073364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6799092888832092},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.56351637840271},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5493277907371521},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5180023312568665},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.5024371147155762},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4867801368236542},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4804810583591461},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4283328652381897},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.15123286843299866},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1076536476612091},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09902924299240112},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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":3,"locations":[{"id":"doi:10.3390/rs14030476","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14030476","pdf_url":"https://www.mdpi.com/2072-4292/14/3/476/pdf?version=1642667263","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:845cddcf92374906aa93f847f2c0bc4f","is_oa":true,"landing_page_url":"https://doaj.org/article/845cddcf92374906aa93f847f2c0bc4f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 3, p 476 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/3/476/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14030476","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 3; Pages: 476","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14030476","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14030476","pdf_url":"https://www.mdpi.com/2072-4292/14/3/476/pdf?version=1642667263","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206194838.pdf","grobid_xml":"https://content.openalex.org/works/W4206194838.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1533861849","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W2012842821","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2124386111","https://openalex.org/W2163605009","https://openalex.org/W2164598857","https://openalex.org/W2168356304","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2463631526","https://openalex.org/W2468396844","https://openalex.org/W2534457893","https://openalex.org/W2565950292","https://openalex.org/W2741077351","https://openalex.org/W2775693072","https://openalex.org/W2891195071","https://openalex.org/W2901911579","https://openalex.org/W2906300491","https://openalex.org/W2911709005","https://openalex.org/W2912067133","https://openalex.org/W2914321566","https://openalex.org/W2945705174","https://openalex.org/W2963351448","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2964209782","https://openalex.org/W2967069910","https://openalex.org/W2985471643","https://openalex.org/W2986661129","https://openalex.org/W3003387640","https://openalex.org/W3006935317","https://openalex.org/W3016663000","https://openalex.org/W3035237998","https://openalex.org/W3090825734","https://openalex.org/W3128592650","https://openalex.org/W3137605476","https://openalex.org/W3169323098","https://openalex.org/W4236219683","https://openalex.org/W4239999461","https://openalex.org/W4243685538","https://openalex.org/W6620707391","https://openalex.org/W6683411478","https://openalex.org/W6684191040","https://openalex.org/W6739901393","https://openalex.org/W6756040250","https://openalex.org/W6763509872","https://openalex.org/W6998817079"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"The":[0,60,148],"number":[1,112],"of":[2,33,62,102,113,125,141,174],"trees":[3,114,198],"and":[4,23,79,104,130,133,138,159],"their":[5,80],"spatial":[6,99],"distribution":[7,100],"are":[8,150],"key":[9],"information":[10,89],"for":[11,53,145,203],"forest":[12,35],"management.":[13],"In":[14,37],"recent":[15],"years,":[16],"deep":[17,45,135],"learning-based":[18],"approaches":[19],"have":[20,179],"been":[21],"proposed":[22],"shown":[24],"promising":[25],"results":[26],"in":[27,115],"lowering":[28],"the":[29,111,175],"expensive":[30],"labor":[31],"cost":[32],"a":[34,42,65,83,93,105,123,153,189,201],"inventory.":[36],"this":[38],"paper,":[39],"we":[40,157],"propose":[41],"new":[43,154,182],"efficient":[44],"learning":[46],"model":[47],"called":[48,184],"density":[49,94,146],"transformer":[50,84],"or":[51],"DENT":[52,63,121,164],"automatic":[54],"tree":[55,107],"counting":[56],"from":[57,76],"aerial":[58],"images.":[59],"architecture":[61],"contains":[64],"multi-receptive":[66],"field":[67],"convolutional":[68,143],"neural":[69,136],"network":[70],"to":[71,86,97,109],"extract":[72],"visual":[73],"feature":[74],"representation":[75],"local":[77],"patches":[78],"wide":[81],"context,":[82],"encoder":[85],"transfer":[87],"contextual":[88],"across":[90],"correlated":[91],"positions,":[92],"map":[95,101],"generator":[96],"generate":[98],"trees,":[103],"fast":[106],"counter":[108],"estimate":[110],"each":[116],"input":[117],"image.":[118],"We":[119,178],"compare":[120],"with":[122,195],"variety":[124],"state-of-art":[126],"methods,":[127],"including":[128],"one-stage":[129],"two-stage,":[131],"anchor-based":[132],"anchor-free":[134],"detectors,":[137],"different":[139],"types":[140],"fully":[142],"regressors":[144],"estimation.":[147],"methods":[149],"evaluated":[151],"on":[152,168],"large":[155],"dataset":[156],"built":[158],"an":[160],"existing":[161],"cross-site":[162],"dataset.":[163],"achieves":[165],"top":[166],"accuracy":[167],"both":[169],"datasets,":[170],"significantly":[171],"outperforming":[172],"most":[173],"other":[176],"methods.":[177],"released":[180],"our":[181],"dataset,":[183],"Yosemite":[185],"Tree":[186],"Dataset,":[187],"containing":[188],"10":[190],"km2":[191],"rectangular":[192],"study":[193],"area":[194],"around":[196],"100k":[197],"annotated,":[199],"as":[200],"benchmark":[202],"public":[204],"access.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3}],"updated_date":"2026-08-02T14:50:37.381335","created_date":"2022-01-25T00:00:00"}
