Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-301-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-301-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal
Sansar Raj Meena
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria
Florian Albrecht
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria
Daniel Hölbling
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria
Omid Ghorbanzadeh
CORRESPONDING AUTHOR
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria
Thomas Blaschke
Interfaculty Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria
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On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
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Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
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The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
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On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
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Short summary
Short summary
Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
L. Abad, D. Hölbling, Z. Dabiri, and B. A. Robson
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Rob Lemmens, Florian Albrecht, Stefan Lang, Sven Casteleyn, Martyna Stelmaszczuk-Górska, Marc Olijslagers, Mariana Belgiu, Carlos Granell, Ellen-Wien Augustijn, Carsten Pathe, Eva-Maria Missoni-Steinbacher, and Aida Monfort Muriach
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Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, https://doi.org/10.5194/nhess-22-1395-2022, 2022
Short summary
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The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
Anne-Laure Argentin, Jörg Robl, Günther Prasicek, Stefan Hergarten, Daniel Hölbling, Lorena Abad, and Zahra Dabiri
Nat. Hazards Earth Syst. Sci., 21, 1615–1637, https://doi.org/10.5194/nhess-21-1615-2021, https://doi.org/10.5194/nhess-21-1615-2021, 2021
Short summary
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This study relies on topography to simulate the origin and displacement of potentially river-blocking landslides. It highlights a continuous range of simulated landslide dams that go unnoticed in the field due to their small scale. The computation results show that landslide-dammed lake volume can be estimated from upstream drainage area and landslide volume, thus enabling an efficient hazard assessment of possible landslide-dammed lake volume – and flooding magnitude in case of dam failure.
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Short summary
Comprehensive and sustainable landslide management, including identification of landslide-susceptible areas, requires a lot of organisations and people to collaborate efficiently. In this study, we propose a concept for a system that provides users with a platform to share the location of landslide events for further collaboration in Nepal. The system can be beneficial for specifying potentially risky regions and consequently, the development of risk mitigation strategies at the local level.
Comprehensive and sustainable landslide management, including identification of...
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