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 article
| 
27 Jul 2023
Data description article |
| 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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Total article views: 5,615 (including HTML, PDF, and XML)
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Total article views: 2,158 (including HTML, PDF, and XML)
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Cited
40 citations as recorded by crossref.
- Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China Y. Song et al.
- Enhancing Sentinel-2 landslide change detection by integrating multispectral, deformation, and topographic information B. Liu et al.
- CS-Mamba: A Novel Mamba-Based Framework for Landslide Detection With Attention Guided Cross-Scale Context Semantic Integration Y. Mao et al.
- A CNN–Transformer hybrid network for efficient cross-region landslide detection by transfer learning Z. Fu et al.
- Adaptive multilayer perceptron optimization for rockfall susceptibility mapping in Huinan County, China Q. Du et al.
- CICRL-FLM: counterfactual inference causal representation learning network for fine-grained landslide mapping from satellite remote sensing images C. Zhao et al.
- LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning G. Liu et al.
- Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain N. Chandra et al.
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al.
- Enhancing landslide detection in Western Ghats of Kerala, India with deep learning and Explainable AI A. Sreekumar et al.
- A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images C. Fang et al.
- A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion K. Wang et al.
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al.
- The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy) D. Notti et al.
- CSLMamba-LM: Mamba-based causal self-contrastive learning network for the fine-grained landslide mapping from very-high-resolution aerial images C. Zhao et al.
- Mamba for Landslide Detection: A Lightweight Model for Mapping Landslides With Very High-Resolution Images X. Tang et al.
- Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion A. Orynbaikyzy et al.
- Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the Novel MRGSLD Dataset G. Emani et al.
- CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images Y. Wang et al.
- LSDSAM: Harnessing Visual Foundation Model and Enhanced Transfer Learning Toward Practical Landslide Detection in Few-Shot Scenarios Z. Fu et al.
- MED-DeepLabv3+: a lightweight landslide recognition algorithm on multi-scale remote sensing images X. Li et al.
- Bright: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response H. Chen et al.
- Multi-modal landslide detection from Sentinel-1 SAR and Sentinel-2 optical imagery using multi-encoder vision transformers and ensemble learning I. Nasios
- Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks L. Nava et al.
- Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas J. Lv et al.
- A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal N. Chandra et al.
- Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions N. Pawar & K. Sharma
- Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically I. Sandric et al.
- A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping J. Yang et al.
- DFG-Net: a dual-stream frequency-guided network for landslide semantic segmentation from remote sensing imagery D. Zhong et al.
- 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.
- A proposed method for landslide detection based on transfer learning and graph neural network W. Luo et al.
- Records of shallow landslides triggered by extreme rainfall in July 2024 in Zixing, China Z. Fu et al.
- A universal adapter in segmentation models for transferable landslide mapping R. Wei et al.
- Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images G. Cheng et al.
- Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery A. Asadi et al.
- CRLMDG-LM: Causal representation learning-guided multi-target domain generalization network for fine-grained landslide mapping from high-resolution remote sensing images C. Zhao et al.
- Topographic and morphological effects of global earthquake- and rainstorm-induced landslides W. Huangfu et al.
- A framework for integrating GPT into geoscience research F. Sufi
- Mapping global post-earthquake ecosystem damage boundaries W. He et al.
40 citations as recorded by crossref.
- Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China Y. Song et al.
- Enhancing Sentinel-2 landslide change detection by integrating multispectral, deformation, and topographic information B. Liu et al.
- CS-Mamba: A Novel Mamba-Based Framework for Landslide Detection With Attention Guided Cross-Scale Context Semantic Integration Y. Mao et al.
- A CNN–Transformer hybrid network for efficient cross-region landslide detection by transfer learning Z. Fu et al.
- Adaptive multilayer perceptron optimization for rockfall susceptibility mapping in Huinan County, China Q. Du et al.
- CICRL-FLM: counterfactual inference causal representation learning network for fine-grained landslide mapping from satellite remote sensing images C. Zhao et al.
- LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning G. Liu et al.
- Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain N. Chandra et al.
- CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Y. Xu et al.
- Enhancing landslide detection in Western Ghats of Kerala, India with deep learning and Explainable AI A. Sreekumar et al.
- A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images C. Fang et al.
- A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion K. Wang et al.
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al.
- The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy) D. Notti et al.
- CSLMamba-LM: Mamba-based causal self-contrastive learning network for the fine-grained landslide mapping from very-high-resolution aerial images C. Zhao et al.
- Mamba for Landslide Detection: A Lightweight Model for Mapping Landslides With Very High-Resolution Images X. Tang et al.
- Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion A. Orynbaikyzy et al.
- Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the Novel MRGSLD Dataset G. Emani et al.
- CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images Y. Wang et al.
- LSDSAM: Harnessing Visual Foundation Model and Enhanced Transfer Learning Toward Practical Landslide Detection in Few-Shot Scenarios Z. Fu et al.
- MED-DeepLabv3+: a lightweight landslide recognition algorithm on multi-scale remote sensing images X. Li et al.
- Bright: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response H. Chen et al.
- Multi-modal landslide detection from Sentinel-1 SAR and Sentinel-2 optical imagery using multi-encoder vision transformers and ensemble learning I. Nasios
- Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks L. Nava et al.
- Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas J. Lv et al.
- A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal N. Chandra et al.
- Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions N. Pawar & K. Sharma
- Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically I. Sandric et al.
- A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping J. Yang et al.
- DFG-Net: a dual-stream frequency-guided network for landslide semantic segmentation from remote sensing imagery D. Zhong et al.
- 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.
- A proposed method for landslide detection based on transfer learning and graph neural network W. Luo et al.
- Records of shallow landslides triggered by extreme rainfall in July 2024 in Zixing, China Z. Fu et al.
- A universal adapter in segmentation models for transferable landslide mapping R. Wei et al.
- Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images G. Cheng et al.
- Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery A. Asadi et al.
- CRLMDG-LM: Causal representation learning-guided multi-target domain generalization network for fine-grained landslide mapping from high-resolution remote sensing images C. Zhao et al.
- Topographic and morphological effects of global earthquake- and rainstorm-induced landslides W. Huangfu et al.
- A framework for integrating GPT into geoscience research F. Sufi
- Mapping global post-earthquake ecosystem damage boundaries W. He et al.
Saved (final revised paper)
Latest update: 06 May 2026
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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