{"id":"https://openalex.org/W2769010475","doi":"https://doi.org/10.1145/3149808.3149814","title":"Recognizing terrain features on terrestrial surface using a deep learning model","display_name":"Recognizing terrain features on terrestrial surface using a deep learning model","publication_year":2017,"publication_date":"2017-11-07","ids":{"openalex":"https://openalex.org/W2769010475","doi":"https://doi.org/10.1145/3149808.3149814","mag":"2769010475"},"language":"en","primary_location":{"id":"doi:10.1145/3149808.3149814","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3149808.3149814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery","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/A5100338179","display_name":"Wenwen Li","orcid":"https://orcid.org/0000-0003-2237-9499"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenwen Li","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049016575","display_name":"Bin Zhou","orcid":"https://orcid.org/0000-0002-1141-5557"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Zhou","raw_affiliation_strings":["Brisky Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Brisky Inc., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047929591","display_name":"Chia-Yu Hsu","orcid":"https://orcid.org/0000-0002-8923-1213"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chia-Yu Hsu","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101493770","display_name":"Yixing Li","orcid":"https://orcid.org/0000-0002-8190-9931"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixing Li","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083744126","display_name":"Fengbo Ren","orcid":"https://orcid.org/0000-0002-6509-8753"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fengbo Ren","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100338179"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":1.0012,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.85436461,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"33","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9930999875068665,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9930999875068665,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12364","display_name":"Archaeological Research and Protection","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1912","display_name":"Space and Planetary Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/impact-crater","display_name":"Impact crater","score":0.9046037197113037},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.846660852432251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6392109990119934},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5967521667480469},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5742059350013733},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5070235729217529},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4725739061832428},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45759400725364685},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4518806040287018},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.4314749538898468},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42739951610565186},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42732715606689453},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4234517514705658},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36564910411834717},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.1311977505683899},{"id":"https://openalex.org/keywords/astrobiology","display_name":"Astrobiology","score":0.11655229330062866},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08509242534637451}],"concepts":[{"id":"https://openalex.org/C179537507","wikidata":"https://www.wikidata.org/wiki/Q55818","display_name":"Impact crater","level":2,"score":0.9046037197113037},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.846660852432251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6392109990119934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5967521667480469},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5742059350013733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5070235729217529},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4725739061832428},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45759400725364685},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4518806040287018},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.4314749538898468},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42739951610565186},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42732715606689453},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4234517514705658},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36564910411834717},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.1311977505683899},{"id":"https://openalex.org/C87355193","wikidata":"https://www.wikidata.org/wiki/Q411","display_name":"Astrobiology","level":1,"score":0.11655229330062866},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08509242534637451},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3149808.3149814","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3149808.3149814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery","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":21,"referenced_works":["https://openalex.org/W259541495","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1601556880","https://openalex.org/W1686810756","https://openalex.org/W1764053936","https://openalex.org/W1849277567","https://openalex.org/W1972819089","https://openalex.org/W2001831758","https://openalex.org/W2063135069","https://openalex.org/W2098676252","https://openalex.org/W2102605133","https://openalex.org/W2108975138","https://openalex.org/W2127592841","https://openalex.org/W2130306094","https://openalex.org/W2151666808","https://openalex.org/W2152229311","https://openalex.org/W2557728737","https://openalex.org/W2604470607","https://openalex.org/W2919115771","https://openalex.org/W4211056572"],"related_works":["https://openalex.org/W2625809971","https://openalex.org/W1663847441","https://openalex.org/W2768493687","https://openalex.org/W2170886505","https://openalex.org/W1969484083","https://openalex.org/W2743845306","https://openalex.org/W2792375831","https://openalex.org/W2386713424","https://openalex.org/W4382051772","https://openalex.org/W1996725752"],"abstract_inverted_index":{"This":[0],"paper":[1],"exploits":[2],"the":[3,12,39,82,94,111],"use":[4],"of":[5,27],"a":[6,24,87,99],"popular":[7],"deep":[8],"learning":[9],"model":[10,96],"-":[11,14],"faster-RCNN":[13,95],"to":[15],"support":[16],"automatic":[17],"terrain":[18],"feature":[19,48],"detection":[20,35],"and":[21,31,72,81],"classification":[22],"using":[23],"mixed":[25],"set":[26],"optimal":[28],"remote":[29],"sensing":[30],"natural":[32],"images.":[33],"Crater":[34],"is":[36],"used":[37,101],"as":[38,57,68],"case":[40],"study":[41],"in":[42,64,107],"this":[43,46],"research":[44],"since":[45],"geomorphological":[47],"provides":[49],"important":[50],"information":[51],"about":[52],"surface":[53],"aging.":[54],"Craters,":[55],"such":[56,67],"impact":[58],"craters,":[59],"also":[60],"effect":[61],"global":[62],"changes":[63],"many":[65],"aspects,":[66],"geography,":[69],"topography,":[70],"mineral":[71],"hydrocarbon":[73],"production,":[74],"etc.":[75],"The":[76],"collected":[77],"data":[78],"were":[79],"labeled":[80],"network":[83,103],"was":[84],"trained":[85],"through":[86],"GPU":[88],"server.":[89],"Experimental":[90],"results":[91],"show":[92],"that":[93],"coupled":[97],"with":[98],"widely":[100],"convolutional":[102],"ZF-net":[104],"performs":[105],"well":[106],"detecting":[108],"craters":[109],"on":[110],"terrestrial":[112],"surface.":[113]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
