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
Autor*in: |
Ofli, Ferda [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s) 2023 |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 35(2023), 24 vom: 24. Mai, Seite 17809-17819 |
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Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:24 ; day:24 ; month:05 ; pages:17809-17819 |
Links: |
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DOI / URN: |
10.1007/s00521-023-08648-0 |
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Katalog-ID: |
OLC2144678278 |
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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. | ||
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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 |
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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 |
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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 |
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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 |
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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 |
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isOA_txt |
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hochschulschrift_bool |
false |
doi_str |
10.1007/s00521-023-08648-0 |
up_date |
2024-07-03T23:46:17.487Z |
_version_ |
1803603544625905664 |
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