{"id":"https://openalex.org/W3171527029","doi":"https://doi.org/10.5194/agile-giss-2-11-2021","title":"Extraction of linear structures from digital terrain models using deep learning","display_name":"Extraction of linear structures from digital terrain models using deep learning","publication_year":2021,"publication_date":"2021-06-04","ids":{"openalex":"https://openalex.org/W3171527029","doi":"https://doi.org/10.5194/agile-giss-2-11-2021","mag":"3171527029"},"language":"en","primary_location":{"id":"doi:10.5194/agile-giss-2-11-2021","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-2-11-2021","pdf_url":"https://agile-giss.copernicus.org/articles/2/11/2021/agile-giss-2-11-2021.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://agile-giss.copernicus.org/articles/2/11/2021/agile-giss-2-11-2021.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092922609","display_name":"Ramish Satari","orcid":"https://orcid.org/0009-0009-1663-3172"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ramish Satari","raw_affiliation_strings":["Institute of Fluid Mechanics and Environmental Physics in Civil Engineering, Leibniz Universit\u00c3\u00a4t Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Fluid Mechanics and Environmental Physics in Civil Engineering, Leibniz Universit\u00c3\u00a4t Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004352063","display_name":"Bashir Kazimi","orcid":"https://orcid.org/0000-0003-1802-7511"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bashir Kazimi","raw_affiliation_strings":["Institute of Cartography and Geoinformatics, Leibniz Universit\u00c3\u00a4t Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geoinformatics, Leibniz Universit\u00c3\u00a4t Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020817045","display_name":"Monika Sester","orcid":"https://orcid.org/0000-0002-6656-8809"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Monika Sester","raw_affiliation_strings":["Institute of Cartography and Geoinformatics, Leibniz Universit\u00c3\u00a4t Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geoinformatics, Leibniz Universit\u00c3\u00a4t Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114112103"],"apc_list":null,"apc_paid":null,"fwci":0.2748,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.51670039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"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":1.0,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9994999766349792,"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":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.7698653340339661},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7637718915939331},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7390407919883728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7265010476112366},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6239230632781982},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.6144877672195435},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5350042581558228},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5336001515388489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5109859704971313},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.46783509850502014},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44869449734687805},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.42677366733551025},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.170855313539505},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12621459364891052}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7698653340339661},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7637718915939331},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7390407919883728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7265010476112366},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6239230632781982},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.6144877672195435},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5350042581558228},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5336001515388489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5109859704971313},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.46783509850502014},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44869449734687805},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.42677366733551025},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.170855313539505},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12621459364891052}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5194/agile-giss-2-11-2021","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-2-11-2021","pdf_url":"https://agile-giss.copernicus.org/articles/2/11/2021/agile-giss-2-11-2021.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5194/agile-giss-2-11-2021","is_oa":true,"landing_page_url":"https://doi.org/10.5194/agile-giss-2-11-2021","pdf_url":"https://agile-giss.copernicus.org/articles/2/11/2021/agile-giss-2-11-2021.pdf","source":{"id":"https://openalex.org/S4210203054","display_name":"AGILE GIScience Series","issn_l":"2700-8150","issn":["2700-8150"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AGILE: GIScience Series","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3171527029.pdf","grobid_xml":"https://content.openalex.org/works/W3171527029.grobid-xml"},"referenced_works_count":73,"referenced_works":["https://openalex.org/W591821005","https://openalex.org/W1522301498","https://openalex.org/W1555328055","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1972556807","https://openalex.org/W1988563578","https://openalex.org/W2073783630","https://openalex.org/W2087884757","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2110764733","https://openalex.org/W2111552444","https://openalex.org/W2119534769","https://openalex.org/W2125215748","https://openalex.org/W2131387345","https://openalex.org/W2151897820","https://openalex.org/W2154579312","https://openalex.org/W2194775991","https://openalex.org/W2278830514","https://openalex.org/W2340897893","https://openalex.org/W2397914484","https://openalex.org/W2412782625","https://openalex.org/W2460456771","https://openalex.org/W2515344238","https://openalex.org/W2531409750","https://openalex.org/W2559966999","https://openalex.org/W2598915960","https://openalex.org/W2609510823","https://openalex.org/W2618530766","https://openalex.org/W2899099629","https://openalex.org/W2899553147","https://openalex.org/W2916798096","https://openalex.org/W2940262938","https://openalex.org/W2945222311","https://openalex.org/W2949650786","https://openalex.org/W2950107101","https://openalex.org/W2951583185","https://openalex.org/W2953139137","https://openalex.org/W2963659230","https://openalex.org/W2963881378","https://openalex.org/W2964121744","https://openalex.org/W2969736172","https://openalex.org/W2969825080","https://openalex.org/W2972093541","https://openalex.org/W2973821635","https://openalex.org/W2979363529","https://openalex.org/W3014641072","https://openalex.org/W3046137190","https://openalex.org/W3047411855","https://openalex.org/W4236340759","https://openalex.org/W4243410576","https://openalex.org/W4308909683","https://openalex.org/W6600007113","https://openalex.org/W6600019104","https://openalex.org/W6600103761","https://openalex.org/W6600168311","https://openalex.org/W6600281463","https://openalex.org/W6600373357","https://openalex.org/W6600433979","https://openalex.org/W6600636991","https://openalex.org/W6601839238","https://openalex.org/W6602411596","https://openalex.org/W6603809027","https://openalex.org/W6604044515","https://openalex.org/W6605997554","https://openalex.org/W6608981203","https://openalex.org/W6743664442","https://openalex.org/W6816747942","https://openalex.org/W6819060087","https://openalex.org/W6968366183","https://openalex.org/W6989086347"],"related_works":["https://openalex.org/W1992962589","https://openalex.org/W2093392189","https://openalex.org/W3032871857","https://openalex.org/W3104633800","https://openalex.org/W2914059119","https://openalex.org/W3023567978","https://openalex.org/W2069184433","https://openalex.org/W3044778482","https://openalex.org/W2743147667","https://openalex.org/W2109698133"],"abstract_inverted_index":{"Abstract.":[0],"This":[1,75,101],"paper":[2,102],"explores":[3],"the":[4,42,63,95,106,114,117,138,147,170,183,188,194,208],"role":[5],"deep":[6,82,197,209],"convolutional":[7,83],"neural":[8,84,198],"networks":[9,121,199],"play":[10],"in":[11,21,53,73,110,185,205],"automated":[12],"extraction":[13],"of":[14,41,65,70,116,169,182,196,202],"linear":[15,130,160],"structures":[16,72,131,204],"using":[17],"semantic":[18,66],"segmentation":[19,67,142],"techniques":[20],"Digital":[22],"Terrain":[23],"Models":[24],"(DTMs).":[25],"DTM":[26,149],"is":[27,143,153],"a":[28,49,159],"regularly":[29],"gridded":[30],"raster":[31],"created":[32],"from":[33],"laser":[34],"scanning":[35],"point":[36],"clouds":[37],"and":[38,80,94,132,179,190],"represents":[39],"elevations":[40],"bare":[43],"earth":[44],"surface":[45],"with":[46],"respect":[47],"to":[48,61,112,124,129,135,155,164,187],"reference.":[50],"Recent":[51],"advances":[52],"Deep":[54],"Learning":[55],"(DL)":[56],"have":[57],"made":[58],"it":[59,166],"possible":[60],"explore":[62],"use":[64],"for":[68,200],"detection":[69,201],"terrain":[71,203],"DTMs.":[74,206],"research":[76],"examines":[77],"two":[78],"novel":[79],"practical":[81],"network":[85,90,99],"architectures":[86],"i.e.":[87],"an":[88],"encoder-decoder":[89],"named":[91],"as":[92,167],"SegNet":[93],"recent":[96],"state-of-the-art":[97],"high-resolution":[98],"(HRNet).":[100],"initially":[103],"focuses":[104],"on":[105,146],"pixel-wise":[107],"binary":[108],"classification":[109],"order":[111],"validate":[113],"applicability":[115,195],"proposed":[118],"approaches.":[119],"The":[120,151],"are":[122],"trained":[123,154],"distinguish":[125],"between":[126],"points":[127],"belonging":[128,134],"those":[133],"background.":[136],"In":[137],"second":[139],"step,":[140],"multi-class":[141],"carried":[144],"out":[145],"same":[148],"dataset.":[150],"model":[152],"not":[156],"only":[157],"detect":[158],"feature,":[161],"but":[162],"also":[163],"categorize":[165],"one":[168],"classes:":[171],"hollow":[172],"ways,":[173],"roads,":[174],"forest":[175],"paths,":[176,178],"historical":[177],"streams.":[180],"Results":[181],"experiment":[184],"addition":[186],"quantitative":[189],"qualitative":[191],"analysis":[192],"show":[193],"From":[207],"learning":[210],"models":[211],"utilized,":[212],"HRNet":[213],"gives":[214],"better":[215],"results.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}