Damage Assessment from Social Media Imagery Data During Disasters
Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this...
Uloženo v:
| Vydáno v: | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) s. 569 - 576 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY, USA
ACM
31.07.2017
|
| Edice: | ACM Conferences |
| Témata: |
Computing methodologies
> Artificial intelligence
> Natural language processing
> Information extraction
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing systems and tools
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing systems and tools
> Social networking sites
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing theory, concepts and paradigms
|
| ISBN: | 1450349935, 9781450349932 |
| ISSN: | 2473-991X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes. |
|---|---|
| AbstractList | Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes. |
| Author | Imran, Muhammad Nguyen, Dat T. Mitra, Prasenjit Ofli, Ferda |
| Author_xml | – sequence: 1 givenname: Dat T. surname: Nguyen fullname: Nguyen, Dat T. email: ndat@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 2 givenname: Ferda surname: Ofli fullname: Ofli, Ferda email: fofli@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 3 givenname: Muhammad surname: Imran fullname: Imran, Muhammad email: mimran@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 4 givenname: Prasenjit surname: Mitra fullname: Mitra, Prasenjit email: pmitra@psu.edu organization: The Pennsylvania State University, University Park, PA, USA |
| BookMark | eNqNkD1PwzAQhs2XRFs6M7B4ZEnxxbEbj1XDR6UiBkBis87upQo0CbLD0H9PonZgZDi90j3P3fCO2XnTNsTYNYgZQKbuJIAQqZoNCcKcsHG_FTIzRqpTNkqzuUyMgY-zv-CSTWP8FP0h5P3IEVsUWOOW-CJGirGmpuNlaGv-2voKd_yZNhXy1aCEPS-wQ178hKrZ8qKKGDsK8YpdlLiLND3mhL0_3L8tn5L1y-NquVgnmILqEi_QlV54KRS4LAdttNvk5DPhNUFO2pW6LNErN5deAagNYa7JuVSD9-jlhN0c_lZEZL9DVWPYWyO0Aal7enug6Gvr2vYrWhB2aMoem7LHpnp19k_VulBRKX8BwNtmJw |
| ContentType | Conference Proceeding |
| Copyright | 2017 ACM |
| Copyright_xml | – notice: 2017 ACM |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1145/3110025.3110109 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Sociology & Social History |
| EISBN | 1450349935 9781450349932 |
| EISSN | 2473-991X |
| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
| Editor_xml | – sequence: 1 givenname: Jana surname: Diesner fullname: Diesner, Jana – sequence: 2 givenname: Elena surname: Ferrari fullname: Ferrari, Elena – sequence: 3 givenname: Guandong surname: Xu fullname: Xu, Guandong |
| EndPage | 576 |
| ExternalDocumentID | 9069136 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAWTH ABLEC ACM ALMA_UNASSIGNED_HOLDINGS APO BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK GUFHI IEGSK LHSKQ OCL RIB RIC RIE RIL |
| ID | FETCH-LOGICAL-a215t-c0abfc0c3051b481696bd8ec40c6e18e6bf6ffac5b73c5115dea86ebb261ccac3 |
| IEDL.DBID | RIE |
| ISBN | 1450349935 9781450349932 |
| IngestDate | Tue Nov 04 05:04:26 EST 2025 Thu Jul 10 05:53:09 EDT 2025 Thu Jul 10 05:53:10 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org |
| LinkModel | DirectLink |
| MeetingName | ASONAM '17: Advances in Social Networks Analysis and Mining 2017 |
| MergedId | FETCHMERGED-LOGICAL-a215t-c0abfc0c3051b481696bd8ec40c6e18e6bf6ffac5b73c5115dea86ebb261ccac3 |
| PageCount | 8 |
| ParticipantIDs | acm_books_10_1145_3110025_3110109_brief acm_books_10_1145_3110025_3110109 ieee_primary_9069136 |
| PublicationCentury | 2000 |
| PublicationDate | 20170731 2017-July |
| PublicationDateYYYYMMDD | 2017-07-31 2017-07-01 |
| PublicationDate_xml | – month: 07 year: 2017 text: 20170731 day: 31 |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY, USA |
| PublicationPlace_xml | – name: New York, NY, USA |
| PublicationSeriesTitle | ACM Conferences |
| PublicationTitle | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) |
| PublicationTitleAbbrev | ASONAM |
| PublicationYear | 2017 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| SSID | ssj0002180213 ssj0001615928 |
| Score | 2.143791 |
| Snippet | Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment... |
| SourceID | ieee acm |
| SourceType | Publisher |
| StartPage | 569 |
| SubjectTerms | Computing methodologies Computing methodologies -- Artificial intelligence Computing methodologies -- Artificial intelligence -- Natural language processing Computing methodologies -- Artificial intelligence -- Natural language processing -- Information extraction Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Machine learning approaches Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social media Information systems Information systems -- Information retrieval Information systems -- Information retrieval -- Retrieval tasks and goals Information systems -- Information retrieval -- Retrieval tasks and goals -- Sentiment analysis Information systems -- Information systems applications |
| Title | Damage Assessment from Social Media Imagery Data During Disasters |
| URI | https://ieeexplore.ieee.org/document/9069136 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pa8IwFH6o7LCTmzqm-0EGY7us2pg2aY4yJ9tFPGzgrSTpK3hQhz8G_vdL0qoMBmOnpuUdype-vpfkfd8DuM8MxplgPBBcYhBlSgaqH0aBdSUaoQ6RSu2bTYjxOJlO5aQCTwcuDCL64jPsuqE_y8-WZuu2ynoy5JIyXoWqELzgah33U2xolmUq7e77TtqMslLNh0Zxjzl1tH7cdVdfgFhVZv6jqYqPKaP6_97mDFpHch6ZHMLOOVRw0YD6vjsDKZ21AZ0DF4U8kIKFSwpRkF0TBkM1t38SMjgIcxJHNNnbueMbRd6cyWpHhmqjyNDzGclwtlZOW2Hdgo_Ry_vza1B2U7Dg03gTmFDp3ITGOjjVUUK55DpL0ESh4UgT5Drnea5MrAUzNg2LM1QJR63tGstOs2EXUFssF3gJBHMTMRabLBd2oKTmubCZEPOtrFGINtxZaFO3TFinBfM5Tkv40xL-Njz-aZPq1QzzNjQd-OlnIb-Rlrh3fn98Bad9F3l9Re011DarLd7AifnazNarW__NfAPUX7w8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB50FfS0PnF9RhC9WG2aR5ujuC4r6rKHFbyVJJ3CHtyVfQj-e5O0rgiCeGpa5lC-dDqTZL5vAM4Ki6JImYxSqTDihVaRTmIeOVeiHE2MVJnQbCLt9bKXF9VfgssFFwYRQ_EZXvlhOMsvxnbut8quVSwVZXIZVgTnSVyxtb53VFxwVnUy7e8TL25GWa3nQ7m4Zl4fLRFX_hpKEJe1ff3RViVElU7zf--zATvf9DzSXwSeTVjC0RY0v_ozkNpdt2B_wUYh56Ti4ZJKFuRjG27a-tX9S8jNQpqTeKrJl50_wNHk3ptMPkhbzzRpB0YjaQ-n2qsrTHfguXM3uO1GdT8FBz8Vs8jG2pQ2ts7FqeEZlUqaIkPLYyuRZihNKctSW2FSZl0iJgrUmURj3CrLTbRlu9AYjUe4BwRLyxkTtihTN9DKyDJ1uRALzawxTVtw6qDN_UJhmlfcZ5HX8Oc1_C24-NMmN5Mhli3Y9uDnb5UAR17jvv_74xNY6w6eHvPH-97DAawnPg6H-tpDaMwmczyCVfs-G04nx-H7-QTPzb-D |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+2017+IEEE%2FACM+International+Conference+on+Advances+in+Social+Networks+Analysis+and+Mining+2017&rft.atitle=Damage+Assessment+from+Social+Media+Imagery+Data+During+Disasters&rft.au=Nguyen%2C+Dat+T.&rft.au=Ofli%2C+Ferda&rft.au=Imran%2C+Muhammad&rft.au=Mitra%2C+Prasenjit&rft.series=ACM+Conferences&rft.date=2017-07-31&rft.pub=ACM&rft.isbn=1450349935&rft.spage=569&rft.epage=576&rft_id=info:doi/10.1145%2F3110025.3110109 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/sc.gif&client=summon&freeimage=true |