{"id":"https://openalex.org/W2756460310","doi":"https://doi.org/10.1109/multi-temp.2017.8035248","title":"On the use of guided regularized random forests to identify crops in smallholder farm fields","display_name":"On the use of guided regularized random forests to identify crops in smallholder farm fields","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2756460310","doi":"https://doi.org/10.1109/multi-temp.2017.8035248","mag":"2756460310"},"language":"en","primary_location":{"id":"doi:10.1109/multi-temp.2017.8035248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/multi-temp.2017.8035248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ris.utwente.nl/ws/files/472675046/On_the_use_of_guided_regularized_random_forests_to_identify_crops_in_smallholder_farm_fields.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082220927","display_name":"Emma Izquierdo\u2010Verdiguier","orcid":"https://orcid.org/0000-0003-2179-1262"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Emma Izquierdo-Verdiguier","raw_affiliation_strings":["ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075715085","display_name":"R. Zurita\u2010Milla","orcid":"https://orcid.org/0000-0002-1769-6310"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Raul Zurita-Milla","raw_affiliation_strings":["ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053690284","display_name":"R.A. de By","orcid":"https://orcid.org/0000-0002-8921-2960"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Rolf A. de By","raw_affiliation_strings":["ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITC - Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":null,"apc_paid":null,"fwci":7.1166,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.96880237,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"abs 1306","issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9972000122070312,"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"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9972000122070312,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9921000003814697,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.9276344776153564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6118216514587402},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6021780967712402},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.581342339515686},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5543861389160156},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5042749643325806},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.44004014134407043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42119884490966797},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40263041853904724},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3939308822154999},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36868226528167725},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3505629301071167},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1369573473930359}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.9276344776153564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6118216514587402},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6021780967712402},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.581342339515686},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5543861389160156},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5042749643325806},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44004014134407043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42119884490966797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40263041853904724},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3939308822154999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36868226528167725},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3505629301071167},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1369573473930359},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/multi-temp.2017.8035248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/multi-temp.2017.8035248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/abf24a29-c2ea-4762-b5c6-81412828ae4e","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/abf24a29-c2ea-4762-b5c6-81412828ae4e","pdf_url":"https://ris.utwente.nl/ws/files/472675046/On_the_use_of_guided_regularized_random_forests_to_identify_crops_in_smallholder_farm_fields.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Izquierdo-Verdiguier, E, Zurita-Milla, R & de By, R A 2017, On the use of guided regularized random forests to identify crops in smallholder farm fields. in Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, Piscataway, NJ, 9th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, MultiTemp 2017, Bruges, Belgium, 26/06/17. https://doi.org/10.1109/Multi-Temp.2017.8035248","raw_type":"info:eu-repo/semantics/bookPart"},{"id":"pmh:oai:ris.utwente.nl:publications/abf24a29-c2ea-4762-b5c6-81412828ae4e","is_oa":true,"landing_page_url":"https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/chap/zuritamilla_on.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Izquierdo-Verdiguier, E, Zurita-Milla, R & de By, R A 2017, On the use of guided regularized random forests to identify crops in smallholder farm fields. in Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, Piscataway, NJ, 9th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, MultiTemp 2017, Bruges, Belgium, 26/06/17. https://doi.org/10.1109/Multi-Temp.2017.8035248","raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/abf24a29-c2ea-4762-b5c6-81412828ae4e","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/abf24a29-c2ea-4762-b5c6-81412828ae4e","pdf_url":"https://ris.utwente.nl/ws/files/472675046/On_the_use_of_guided_regularized_random_forests_to_identify_crops_in_smallholder_farm_fields.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Izquierdo-Verdiguier, E, Zurita-Milla, R & de By, R A 2017, On the use of guided regularized random forests to identify crops in smallholder farm fields. in Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, Piscataway, NJ, 9th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, MultiTemp 2017, Bruges, Belgium, 26/06/17. https://doi.org/10.1109/Multi-Temp.2017.8035248","raw_type":"info:eu-repo/semantics/bookPart"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2756460310.pdf","grobid_xml":"https://content.openalex.org/works/W2756460310.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1484777458","https://openalex.org/W1546989560","https://openalex.org/W2018627383","https://openalex.org/W2022604714","https://openalex.org/W2091888740","https://openalex.org/W2155632266","https://openalex.org/W2171730988","https://openalex.org/W2911964244","https://openalex.org/W6632791947"],"related_works":["https://openalex.org/W4296209631","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W4388745254","https://openalex.org/W2889302474","https://openalex.org/W2980082554","https://openalex.org/W1517228774","https://openalex.org/W2767419625","https://openalex.org/W2389704471","https://openalex.org/W2117019857"],"abstract_inverted_index":{"The":[0,43],"smallholder":[1,24],"farms":[2],"located":[3],"in":[4],"sub-Sahara":[5],"Africa":[6],"are":[7],"typically":[8],"characterize":[9],"by":[10,128,138,142,147],"heterogeneous":[11],"mosaic":[12],"of":[13,35,45,64,75],"crops,":[14],"soils,":[15],"weather":[16],"and":[17,131],"farm":[18],"practices.":[19],"Automatic":[20],"crop":[21],"identification":[22,88],"over":[23],"fields":[25],"is":[26,48,78],"challenging":[27],"when":[28],"one":[29,80],"only":[30],"uses":[31,153],"the":[32,83,103,132,145,155],"spectral":[33],"information":[34,47],"very":[36],"high":[37],"spatial":[38],"resolution":[39],"image":[40,69],"time":[41,70],"series.":[42,71],"extraction":[44],"spatial-spectral":[46],"important":[49,85],"to":[50,59,81],"reach":[51,124],"classifier":[52,151],"accuracy.":[53],"We":[54],"deploy":[55],"cloud":[56],"computing":[57],"techniques":[58],"allow":[60],"working":[61],"with":[62],"thousands":[63],"features":[65,77,86,140],"derived":[66],"from":[67],"an":[68,125],"However,":[72],"this":[73],"number":[74],"extracted":[76],"forces":[79],"select":[82],"most":[84],"for":[87],"routines.":[89],"This":[90],"paper":[91],"introduces":[92],"a":[93,148],"simple":[94],"feature":[95,112],"selection":[96],"method":[97],"based":[98],"on":[99],"Random":[100,106,149],"Forest":[101,107,150],"-":[102,109],"Guided":[104],"Regularized":[105],"(GRRF)":[108],"which":[110],"reduces":[111],"dimensionality":[113],"without":[114],"loss":[115],"data":[116],"information.":[117],"Preliminary":[118],"experiments":[119],"show":[120],"that":[121,152],"we":[122],"can":[123],"overall":[126],"accuracy":[127],"around":[129,143],"63%,":[130],"results":[133,146],"using":[134],"random":[135],"forests":[136],"trained":[137],"GRRF":[139],"improve":[141],"2.5%":[144],"all":[154],"features.":[156]},"counts_by_year":[{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}