{"id":"https://openalex.org/W4387828717","doi":"https://doi.org/10.1109/igarss52108.2023.10281912","title":"Efficient Large-Scale Damage Assessment After Natural Disasters With UAVS and Deep Learning","display_name":"Efficient Large-Scale Damage Assessment After Natural Disasters With UAVS and Deep Learning","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387828717","doi":"https://doi.org/10.1109/igarss52108.2023.10281912"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010792548","display_name":"Maryam Rahnemoonfar","orcid":"https://orcid.org/0000-0001-9358-2836"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryam Rahnemoonfar","raw_affiliation_strings":["Lehigh University,Department of Computer Science and Engineering,PA,USA","Department of Civil and Environmental Engineering, Lehigh University, PA, USA","Department of Computer Science and Engineering, Lehigh University, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lehigh University,Department of Computer Science and Engineering,PA,USA","institution_ids":["https://openalex.org/I186143895"]},{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Lehigh University, PA, USA","institution_ids":["https://openalex.org/I186143895"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Lehigh University, PA, USA","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023316624","display_name":"Farshad Safavi","orcid":"https://orcid.org/0000-0002-6905-0781"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farshad Safavi","raw_affiliation_strings":["University of Maryland,Department of Computer Science,Baltimore County,USA","Department of Computer Science, University of Maryland, Baltimore County, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,Department of Computer Science,Baltimore County,USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"Department of Computer Science, University of Maryland, Baltimore County, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8712,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75602675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1668","last_page":"1671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9940000176429749,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7899090051651001},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6479102373123169},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6234628558158875},{"id":"https://openalex.org/keywords/natural-disaster","display_name":"Natural disaster","score":0.5494064092636108},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.47574684023857117},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4423445463180542},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4317641258239746},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.42470183968544006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4239177107810974},{"id":"https://openalex.org/keywords/emergency-management","display_name":"Emergency management","score":0.41459977626800537},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35949504375457764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7899090051651001},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6479102373123169},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6234628558158875},{"id":"https://openalex.org/C166566181","wikidata":"https://www.wikidata.org/wiki/Q8065","display_name":"Natural disaster","level":2,"score":0.5494064092636108},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47574684023857117},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4423445463180542},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4317641258239746},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.42470183968544006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4239177107810974},{"id":"https://openalex.org/C62555980","wikidata":"https://www.wikidata.org/wiki/Q1460420","display_name":"Emergency management","level":2,"score":0.41459977626800537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35949504375457764},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10281912","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281912","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2560023338","https://openalex.org/W2886934227","https://openalex.org/W2901139858","https://openalex.org/W2963446712","https://openalex.org/W2964309882","https://openalex.org/W2982083293","https://openalex.org/W2987175876","https://openalex.org/W3122445164","https://openalex.org/W3162524694","https://openalex.org/W3193876931","https://openalex.org/W3196904463","https://openalex.org/W3207025788","https://openalex.org/W3207809295","https://openalex.org/W3211490618","https://openalex.org/W4205569865","https://openalex.org/W4293406525","https://openalex.org/W4308236544","https://openalex.org/W4311081271","https://openalex.org/W4376607469","https://openalex.org/W6717372056","https://openalex.org/W6748481559","https://openalex.org/W6776044926","https://openalex.org/W6789654255","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2383619049","https://openalex.org/W2366388585","https://openalex.org/W1061078080","https://openalex.org/W3173104482"],"abstract_inverted_index":{"Frequent":[0],"and":[1,13,20,42,81,92,108,111],"increasingly":[2],"severe":[3],"natural":[4],"disasters":[5],"due":[6],"to":[7,26],"climate":[8],"change":[9],"threaten":[10],"human":[11],"health":[12],"infrastructure.":[14],"The":[15,52],"provision":[16],"of":[17,45,58,69,72],"accurate,":[18],"timely,":[19],"understandable":[21],"information":[22],"has":[23,65],"the":[24,37,39,46,56],"potential":[25],"revolutionize":[27],"disaster":[28],"management.":[29],"While":[30],"traditional":[31],"analyses":[32],"provide":[33],"some":[34],"insights":[35],"into":[36],"data,":[38],"complexity,":[40],"scale,":[41],"multi-disciplinary":[43],"nature":[44],"data":[47,61],"necessitate":[48],"advanced,":[49],"intelligent":[50],"solutions.":[51],"main":[53],"barrier":[54],"is":[55],"lack":[57],"near":[59],"real-time":[60,73],"analysis.":[62],"Recently":[63],"there":[64],"been":[66],"a":[67,85,116],"surge":[68],"practical":[70],"applications":[71],"semantic":[74,77,99],"segmentation.":[75,100],"Real-time":[76],"segmentation":[78],"requires":[79],"fast":[80],"high-quality":[82],"predictions.":[83],"As":[84],"result,":[86],"lightweight":[87],"architectures":[88,110],"with":[89],"low":[90],"latency":[91],"computational":[93],"costs":[94],"are":[95],"necessary":[96],"for":[97],"efficient":[98],"In":[101],"this":[102],"article,":[103],"we":[104],"developed":[105],"several":[106],"encoder-decoder":[107],"two-pathway":[109],"compared":[112],"their":[113],"performance":[114],"on":[115],"novel":[117],"RescuNet":[118],"dataset.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}