{"id":"https://openalex.org/W2913542514","doi":"https://doi.org/10.1109/bigdata.2018.8622562","title":"Toward Machine Learning on Granulated Data \u2013 a Case of Compact Autoencoder-based Representations of Satellite Images","display_name":"Toward Machine Learning on Granulated Data \u2013 a Case of Compact Autoencoder-based Representations of Satellite Images","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913542514","doi":"https://doi.org/10.1109/bigdata.2018.8622562","mag":"2913542514"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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":"https://openalex.org/A5073580921","display_name":"Mateusz Przyborowski","orcid":"https://orcid.org/0000-0002-7721-8433"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mateusz Przyborowski","raw_affiliation_strings":["eSensei Sp. z o.o., Poland"],"affiliations":[{"raw_affiliation_string":"eSensei Sp. z o.o., Poland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027080461","display_name":"Tomasz Tajmajer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomasz Tajmajer","raw_affiliation_strings":["eSensei Sp. z o.o., Poland"],"affiliations":[{"raw_affiliation_string":"eSensei Sp. z o.o., Poland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064956784","display_name":"\u0141ukasz Grad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lukasz Grad","raw_affiliation_strings":["eSensei Sp. z o.o., Poland"],"affiliations":[{"raw_affiliation_string":"eSensei Sp. z o.o., Poland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006523416","display_name":"Andrzej Janusz","orcid":"https://orcid.org/0000-0002-9763-1399"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrzej Janusz","raw_affiliation_strings":["eSensei Sp. z o.o., Poland"],"affiliations":[{"raw_affiliation_string":"eSensei Sp. z o.o., Poland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044238055","display_name":"Piotr Biczyk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Piotr Biczyk","raw_affiliation_strings":["eSensei Sp. z o.o., Poland"],"affiliations":[{"raw_affiliation_string":"eSensei Sp. z o.o., Poland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057541763","display_name":"Dominik \u015al\u0229zak","orcid":"https://orcid.org/0000-0003-2453-4974"},"institutions":[{"id":"https://openalex.org/I4654613","display_name":"University of Warsaw","ror":"https://ror.org/039bjqg32","country_code":"PL","type":"education","lineage":["https://openalex.org/I4654613"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Dominik Slezak","raw_affiliation_strings":["Institute of Informatics, University of Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Institute of Informatics, University of Warsaw, Poland","institution_ids":["https://openalex.org/I4654613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5073580921"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8357,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.79636191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2657","last_page":"2662"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9977999925613403,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9977999925613403,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9966999888420105,"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":0.9955999851226807,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8964362740516663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6566238403320312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6517655849456787},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.6255647540092468},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46209755539894104},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44961777329444885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3719879686832428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36232471466064453},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3425036668777466},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34242701530456543},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3273574411869049},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11633369326591492},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0687919557094574}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8964362740516663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6566238403320312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6517655849456787},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.6255647540092468},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46209755539894104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44961777329444885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3719879686832428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36232471466064453},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3425036668777466},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34242701530456543},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3273574411869049},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11633369326591492},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0687919557094574},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1980038761","https://openalex.org/W2001027782","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2396976214","https://openalex.org/W2527747047","https://openalex.org/W2593493485","https://openalex.org/W2775795276","https://openalex.org/W2798309714","https://openalex.org/W2962790638","https://openalex.org/W2963149687","https://openalex.org/W2963449488","https://openalex.org/W2963849356","https://openalex.org/W2964121744","https://openalex.org/W2964180752","https://openalex.org/W4294567867","https://openalex.org/W4297659253","https://openalex.org/W6631190155","https://openalex.org/W6694251005","https://openalex.org/W6712185225","https://openalex.org/W6726139862","https://openalex.org/W6728252283","https://openalex.org/W6734035190","https://openalex.org/W6738494155","https://openalex.org/W6747218270","https://openalex.org/W6751788378"],"related_works":["https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W3044458868","https://openalex.org/W2538028360","https://openalex.org/W3165463024","https://openalex.org/W2592385986","https://openalex.org/W2775464024","https://openalex.org/W4287178339","https://openalex.org/W2404595106","https://openalex.org/W4213225422"],"abstract_inverted_index":{"We":[0,21,67],"consider":[1],"a":[2,12,23],"problem":[3],"of":[4,9,14,50,57,65,72],"learning":[5],"from":[6,42],"compact":[7],"representations":[8],"images":[10,28,79,85],"for":[11,25],"purpose":[13],"object":[15],"recognition":[16],"and":[17,32,83],"content-based":[18],"image":[19,59],"retrieval.":[20],"discuss":[22],"motivation":[24],"using":[26,81],"compressed":[27,80],"in":[29,90],"those":[30],"tasks":[31],"indicate":[33],"exemplary":[34],"applications":[35],"related":[36],"to":[37,53,92],"analysis":[38],"on":[39,62,76,84],"the":[40,55,58,63,70],"data":[41,60],"satellites.":[43],"Finally,":[44],"we":[45],"show":[46],"some":[47],"preliminary":[48],"results":[49],"experiments":[51],"conducted":[52],"demonstrate":[54],"impact":[56],"granulation":[61],"quality":[64,87],"classification.":[66],"empirically":[68],"compare":[69],"performance":[71],"prediction":[73],"models":[74],"trained":[75],"original":[77],"images,":[78],"autoencoders,":[82],"whose":[86],"was":[88],"lowered":[89],"order":[91],"reduce":[93],"their":[94],"size.":[95]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
