Landslide detection in real-time social media image streams
Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-tradition...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ofli, Ferda [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s) 2023 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 35(2023), 24 vom: 24. Mai, Seite 17809-17819 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:24 ; day:24 ; month:05 ; pages:17809-17819 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-023-08648-0 |
|---|
| Katalog-ID: |
OLC2144678278 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2144678278 | ||
| 003 | DE-627 | ||
| 005 | 20240118100333.0 | ||
| 007 | tu | ||
| 008 | 240118s2023 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00521-023-08648-0 |2 doi | |
| 035 | |a (DE-627)OLC2144678278 | ||
| 035 | |a (DE-He213)s00521-023-08648-0-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Ofli, Ferda |e verfasserin |0 (orcid)0000-0003-3918-3230 |4 aut | |
| 245 | 1 | 0 | |a Landslide detection in real-time social media image streams |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s) 2023 | ||
| 520 | |a Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. | ||
| 650 | 4 | |a Landslide detection | |
| 650 | 4 | |a Social media | |
| 650 | 4 | |a Image classification | |
| 650 | 4 | |a Data-centric AI | |
| 700 | 1 | |a Imran, Muhammad |0 (orcid)0000-0001-7882-5502 |4 aut | |
| 700 | 1 | |a Qazi, Umair |0 (orcid)0000-0002-2448-9694 |4 aut | |
| 700 | 1 | |a Roch, Julien |0 (orcid)0000-0002-2655-9107 |4 aut | |
| 700 | 1 | |a Pennington, Catherine |0 (orcid)0000-0002-3560-9030 |4 aut | |
| 700 | 1 | |a Banks, Vanessa |0 (orcid)0000-0001-6335-7080 |4 aut | |
| 700 | 1 | |a Bossu, Remy |0 (orcid)0000-0002-9927-9122 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural computing & applications |d Springer London, 1993 |g 35(2023), 24 vom: 24. Mai, Seite 17809-17819 |w (DE-627)165669608 |w (DE-600)1136944-9 |w (DE-576)032873050 |x 0941-0643 |7 nnns |
| 773 | 1 | 8 | |g volume:35 |g year:2023 |g number:24 |g day:24 |g month:05 |g pages:17809-17819 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00521-023-08648-0 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4277 | ||
| 951 | |a AR | ||
| 952 | |d 35 |j 2023 |e 24 |b 24 |c 05 |h 17809-17819 | ||
| author_variant |
f o fo m i mi u q uq j r jr c p cp v b vb r b rb |
|---|---|
| matchkey_str |
article:09410643:2023----::adlddtcinnelieoile |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s00521-023-08648-0 doi (DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p DE-627 ger DE-627 rakwb eng 004 VZ Ofli, Ferda verfasserin (orcid)0000-0003-3918-3230 aut Landslide detection in real-time social media image streams 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. Landslide detection Social media Image classification Data-centric AI Imran, Muhammad (orcid)0000-0001-7882-5502 aut Qazi, Umair (orcid)0000-0002-2448-9694 aut Roch, Julien (orcid)0000-0002-2655-9107 aut Pennington, Catherine (orcid)0000-0002-3560-9030 aut Banks, Vanessa (orcid)0000-0001-6335-7080 aut Bossu, Remy (orcid)0000-0002-9927-9122 aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 24 vom: 24. Mai, Seite 17809-17819 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 https://doi.org/10.1007/s00521-023-08648-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 24 24 05 17809-17819 |
| spelling |
10.1007/s00521-023-08648-0 doi (DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p DE-627 ger DE-627 rakwb eng 004 VZ Ofli, Ferda verfasserin (orcid)0000-0003-3918-3230 aut Landslide detection in real-time social media image streams 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. Landslide detection Social media Image classification Data-centric AI Imran, Muhammad (orcid)0000-0001-7882-5502 aut Qazi, Umair (orcid)0000-0002-2448-9694 aut Roch, Julien (orcid)0000-0002-2655-9107 aut Pennington, Catherine (orcid)0000-0002-3560-9030 aut Banks, Vanessa (orcid)0000-0001-6335-7080 aut Bossu, Remy (orcid)0000-0002-9927-9122 aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 24 vom: 24. Mai, Seite 17809-17819 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 https://doi.org/10.1007/s00521-023-08648-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 24 24 05 17809-17819 |
| allfields_unstemmed |
10.1007/s00521-023-08648-0 doi (DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p DE-627 ger DE-627 rakwb eng 004 VZ Ofli, Ferda verfasserin (orcid)0000-0003-3918-3230 aut Landslide detection in real-time social media image streams 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. Landslide detection Social media Image classification Data-centric AI Imran, Muhammad (orcid)0000-0001-7882-5502 aut Qazi, Umair (orcid)0000-0002-2448-9694 aut Roch, Julien (orcid)0000-0002-2655-9107 aut Pennington, Catherine (orcid)0000-0002-3560-9030 aut Banks, Vanessa (orcid)0000-0001-6335-7080 aut Bossu, Remy (orcid)0000-0002-9927-9122 aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 24 vom: 24. Mai, Seite 17809-17819 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 https://doi.org/10.1007/s00521-023-08648-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 24 24 05 17809-17819 |
| allfieldsGer |
10.1007/s00521-023-08648-0 doi (DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p DE-627 ger DE-627 rakwb eng 004 VZ Ofli, Ferda verfasserin (orcid)0000-0003-3918-3230 aut Landslide detection in real-time social media image streams 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. Landslide detection Social media Image classification Data-centric AI Imran, Muhammad (orcid)0000-0001-7882-5502 aut Qazi, Umair (orcid)0000-0002-2448-9694 aut Roch, Julien (orcid)0000-0002-2655-9107 aut Pennington, Catherine (orcid)0000-0002-3560-9030 aut Banks, Vanessa (orcid)0000-0001-6335-7080 aut Bossu, Remy (orcid)0000-0002-9927-9122 aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 24 vom: 24. Mai, Seite 17809-17819 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 https://doi.org/10.1007/s00521-023-08648-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 24 24 05 17809-17819 |
| allfieldsSound |
10.1007/s00521-023-08648-0 doi (DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p DE-627 ger DE-627 rakwb eng 004 VZ Ofli, Ferda verfasserin (orcid)0000-0003-3918-3230 aut Landslide detection in real-time social media image streams 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. Landslide detection Social media Image classification Data-centric AI Imran, Muhammad (orcid)0000-0001-7882-5502 aut Qazi, Umair (orcid)0000-0002-2448-9694 aut Roch, Julien (orcid)0000-0002-2655-9107 aut Pennington, Catherine (orcid)0000-0002-3560-9030 aut Banks, Vanessa (orcid)0000-0001-6335-7080 aut Bossu, Remy (orcid)0000-0002-9927-9122 aut Enthalten in Neural computing & applications Springer London, 1993 35(2023), 24 vom: 24. Mai, Seite 17809-17819 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 https://doi.org/10.1007/s00521-023-08648-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2023 24 24 05 17809-17819 |
| language |
English |
| source |
Enthalten in Neural computing & applications 35(2023), 24 vom: 24. Mai, Seite 17809-17819 volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 |
| sourceStr |
Enthalten in Neural computing & applications 35(2023), 24 vom: 24. Mai, Seite 17809-17819 volume:35 year:2023 number:24 day:24 month:05 pages:17809-17819 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Landslide detection Social media Image classification Data-centric AI |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Neural computing & applications |
| authorswithroles_txt_mv |
Ofli, Ferda @@aut@@ Imran, Muhammad @@aut@@ Qazi, Umair @@aut@@ Roch, Julien @@aut@@ Pennington, Catherine @@aut@@ Banks, Vanessa @@aut@@ Bossu, Remy @@aut@@ |
| publishDateDaySort_date |
2023-05-24T00:00:00Z |
| hierarchy_top_id |
165669608 |
| dewey-sort |
14 |
| id |
OLC2144678278 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144678278</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118100333.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-023-08648-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144678278</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-023-08648-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ofli, Ferda</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3918-3230</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Landslide detection in real-time social media image streams</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landslide detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social media</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-centric AI</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Imran, Muhammad</subfield><subfield code="0">(orcid)0000-0001-7882-5502</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qazi, Umair</subfield><subfield code="0">(orcid)0000-0002-2448-9694</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roch, Julien</subfield><subfield code="0">(orcid)0000-0002-2655-9107</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pennington, Catherine</subfield><subfield code="0">(orcid)0000-0002-3560-9030</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Banks, Vanessa</subfield><subfield code="0">(orcid)0000-0001-6335-7080</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bossu, Remy</subfield><subfield code="0">(orcid)0000-0002-9927-9122</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">35(2023), 24 vom: 24. Mai, Seite 17809-17819</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:24</subfield><subfield code="g">day:24</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:17809-17819</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-023-08648-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2023</subfield><subfield code="e">24</subfield><subfield code="b">24</subfield><subfield code="c">05</subfield><subfield code="h">17809-17819</subfield></datafield></record></collection>
|
| author |
Ofli, Ferda |
| spellingShingle |
Ofli, Ferda ddc 004 misc Landslide detection misc Social media misc Image classification misc Data-centric AI Landslide detection in real-time social media image streams |
| authorStr |
Ofli, Ferda |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)165669608 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0941-0643 |
| topic_title |
004 VZ Landslide detection in real-time social media image streams Landslide detection Social media Image classification Data-centric AI |
| topic |
ddc 004 misc Landslide detection misc Social media misc Image classification misc Data-centric AI |
| topic_unstemmed |
ddc 004 misc Landslide detection misc Social media misc Image classification misc Data-centric AI |
| topic_browse |
ddc 004 misc Landslide detection misc Social media misc Image classification misc Data-centric AI |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural computing & applications |
| hierarchy_parent_id |
165669608 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural computing & applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 |
| title |
Landslide detection in real-time social media image streams |
| ctrlnum |
(DE-627)OLC2144678278 (DE-He213)s00521-023-08648-0-p |
| title_full |
Landslide detection in real-time social media image streams |
| author_sort |
Ofli, Ferda |
| journal |
Neural computing & applications |
| journalStr |
Neural computing & applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
17809 |
| author_browse |
Ofli, Ferda Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa Bossu, Remy |
| container_volume |
35 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Ofli, Ferda |
| doi_str_mv |
10.1007/s00521-023-08648-0 |
| normlink |
(ORCID)0000-0003-3918-3230 (ORCID)0000-0001-7882-5502 (ORCID)0000-0002-2448-9694 (ORCID)0000-0002-2655-9107 (ORCID)0000-0002-3560-9030 (ORCID)0000-0001-6335-7080 (ORCID)0000-0002-9927-9122 |
| normlink_prefix_str_mv |
(orcid)0000-0003-3918-3230 (orcid)0000-0001-7882-5502 (orcid)0000-0002-2448-9694 (orcid)0000-0002-2655-9107 (orcid)0000-0002-3560-9030 (orcid)0000-0001-6335-7080 (orcid)0000-0002-9927-9122 |
| dewey-full |
004 |
| title_sort |
landslide detection in real-time social media image streams |
| title_auth |
Landslide detection in real-time social media image streams |
| abstract |
Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. © The Author(s) 2023 |
| abstractGer |
Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. © The Author(s) 2023 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
24 |
| title_short |
Landslide detection in real-time social media image streams |
| url |
https://doi.org/10.1007/s00521-023-08648-0 |
| remote_bool |
false |
| author2 |
Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa Bossu, Remy |
| author2Str |
Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa Bossu, Remy |
| ppnlink |
165669608 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-023-08648-0 |
| up_date |
2024-07-03T23:46:17.487Z |
| _version_ |
1803603544625905664 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144678278</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118100333.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-023-08648-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144678278</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-023-08648-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ofli, Ferda</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3918-3230</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Landslide detection in real-time social media image streams</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landslide detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social media</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-centric AI</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Imran, Muhammad</subfield><subfield code="0">(orcid)0000-0001-7882-5502</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qazi, Umair</subfield><subfield code="0">(orcid)0000-0002-2448-9694</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roch, Julien</subfield><subfield code="0">(orcid)0000-0002-2655-9107</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pennington, Catherine</subfield><subfield code="0">(orcid)0000-0002-3560-9030</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Banks, Vanessa</subfield><subfield code="0">(orcid)0000-0001-6335-7080</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bossu, Remy</subfield><subfield code="0">(orcid)0000-0002-9927-9122</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">35(2023), 24 vom: 24. Mai, Seite 17809-17819</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:24</subfield><subfield code="g">day:24</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:17809-17819</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-023-08648-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2023</subfield><subfield code="e">24</subfield><subfield code="b">24</subfield><subfield code="c">05</subfield><subfield code="h">17809-17819</subfield></datafield></record></collection>
|
| score |
7.3987026 |