{"id":"https://openalex.org/W2900600627","doi":"https://doi.org/10.1109/igarss.2018.8518917","title":"Inversion of Deep Networks for Modelling Variations in Spatial Distributions of Land Cover Classes Across Scales","display_name":"Inversion of Deep Networks for Modelling Variations in Spatial Distributions of Land Cover Classes Across Scales","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2900600627","doi":"https://doi.org/10.1109/igarss.2018.8518917","mag":"2900600627"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8518917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","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/A5101882374","display_name":"P. V. Arun","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P.V. Arun","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059951735","display_name":"Krishna Mohan Buddhiraju","orcid":"https://orcid.org/0000-0001-6815-8988"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krishna Mohan Buddhiraju","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011288887","display_name":"Alok Porwal","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Alok Porwal","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay","institution_ids":["https://openalex.org/I162827531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1953,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62261312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"7129","last_page":"7132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9976999759674072,"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":0.9976999759674072,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9945999979972839,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.7697267532348633},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6552834510803223},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.619369626045227},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5863572359085083},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4804398715496063},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4624735414981842},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41873660683631897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4004695415496826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3650533854961395},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33101755380630493},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.23518779873847961},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1608026921749115},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.14488360285758972},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09469401836395264}],"concepts":[{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.7697267532348633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6552834510803223},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.619369626045227},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5863572359085083},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4804398715496063},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4624735414981842},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41873660683631897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4004695415496826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3650533854961395},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33101755380630493},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.23518779873847961},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1608026921749115},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.14488360285758972},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09469401836395264},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8518917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1915485278","https://openalex.org/W1973567017","https://openalex.org/W2004815533","https://openalex.org/W2124964692","https://openalex.org/W2154815154","https://openalex.org/W2240067561","https://openalex.org/W2327302159","https://openalex.org/W2509277247","https://openalex.org/W2548252668","https://openalex.org/W2765473431","https://openalex.org/W2963989815","https://openalex.org/W4231342789","https://openalex.org/W6678856934","https://openalex.org/W6689886725"],"related_works":["https://openalex.org/W3135697610","https://openalex.org/W2044092692","https://openalex.org/W2088899772","https://openalex.org/W2614621130","https://openalex.org/W2547665164","https://openalex.org/W3103111272","https://openalex.org/W4289655544","https://openalex.org/W2615806692","https://openalex.org/W2080965330","https://openalex.org/W2547534968"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"the":[5,12,20,23,28,38,54,62,66,72,79,82,87,97,100,108,111,123,130],"use":[6],"of":[7,14,22,57,81,91,99,113],"network":[8,59,83],"inversion":[9,60],"for":[10,49,61],"modeling":[11],"variation":[13],"class":[15],"distributions":[16,39],"with":[17,78,107],"scale.":[18],"Unlike":[19],"state":[21],"art":[24],"methods":[25],"that":[26,70,122],"predict":[27],"mapping":[29],"between":[30],"coarser":[31,41,46],"and":[32,75,89],"finer":[33],"scale":[34,47],"patches":[35],"without":[36],"considering":[37],"at":[40],"scale,":[42],"our":[43],"approach":[44,101,125],"uses":[45],"features":[48],"effective":[50],"reconstruction.":[51],"This":[52],"is":[53],"pioneer":[55],"work":[56],"using":[58],"purpose.":[63],"Analysis":[64],"over":[65,118],"proposed":[67,124],"framework":[68],"reveals":[69],"both":[71],"computational":[73],"performance":[74,98],"accuracy":[76],"varies":[77],"depth":[80],"as":[84,86,134,136],"well":[85,135],"size":[88],"number":[90,112],"filters":[92],"in":[93,110],"each":[94],"layer.":[95],"Also":[96],"has":[102],"been":[103],"found":[104],"to":[105],"improve":[106],"increase":[109],"input":[114],"feature":[115],"maps.":[116],"Investigations":[117],"standard":[119],"datasets":[120],"indicate":[121],"performs":[126],"much":[127],"better":[128],"than":[129],"recent":[131],"sub-pixel":[132],"classification":[133],"super":[137],"resolution":[138],"techniques.":[139]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}