Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jul 2023
Data description paper |
| 27 Jul 2023
| 27 Jul 2023
HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery
Sansar Raj Meena
CORRESPONDING AUTHOR
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Lorenzo Nava
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Kushanav Bhuyan
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Silvia Puliero
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Lucas Pedrosa Soares
Institute of Energy and Environment, University of São Paulo, 05508-010 São Paulo , Brazil
Helen Cristina Dias
Institute of Energy and Environment, University of São Paulo, 05508-010 São Paulo , Brazil
Mario Floris
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Filippo Catani
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
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Cited
13 citations as recorded by crossref.
- Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China Y. Song et al. 10.1080/19475705.2024.2354499
- A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion K. Wang et al. 10.1016/j.asoc.2024.111542
- Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images G. Cheng et al. 10.3390/rs16101787
- The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy) D. Notti et al. 10.1016/j.jag.2024.103806
- Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery A. Asadi et al. 10.1080/17499518.2024.2316265
- Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically I. Sandric et al. 10.1007/s10346-024-02295-9
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al. 10.1038/s41597-023-02847-z
- Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features L. Wu et al. 10.1016/j.jag.2023.103612
- Evaluation of U-Net transfer learning model for semantic segmentation of landslides in the Colombian tropical mountain region J. Vega et al. 10.1051/matecconf/202439619002
- A novel Dynahead-Yolo neural network for the detection of landslides with variable proportions using remote sensing images Z. Han et al. 10.3389/feart.2022.1077153
- HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery S. Meena et al. 10.5194/essd-15-3283-2023
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya 10.1007/s12040-024-02281-8
8 citations as recorded by crossref.
- Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China Y. Song et al. 10.1080/19475705.2024.2354499
- A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion K. Wang et al. 10.1016/j.asoc.2024.111542
- Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images G. Cheng et al. 10.3390/rs16101787
- The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy) D. Notti et al. 10.1016/j.jag.2024.103806
- Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery A. Asadi et al. 10.1080/17499518.2024.2316265
- Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically I. Sandric et al. 10.1007/s10346-024-02295-9
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al. 10.1038/s41597-023-02847-z
- Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features L. Wu et al. 10.1016/j.jag.2023.103612
5 citations as recorded by crossref.
- Evaluation of U-Net transfer learning model for semantic segmentation of landslides in the Colombian tropical mountain region J. Vega et al. 10.1051/matecconf/202439619002
- A novel Dynahead-Yolo neural network for the detection of landslides with variable proportions using remote sensing images Z. Han et al. 10.3389/feart.2022.1077153
- HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery S. Meena et al. 10.5194/essd-15-3283-2023
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya 10.1007/s12040-024-02281-8
Latest update: 29 Jun 2024
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.
Landslides occur often across the world, with the potential to cause significant damage....
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