Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection-Reference-Cited by-同舟云学术

Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection

Published:2024 Issue: Volume:12 Page:118453-118466
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ghorbanzadeh Omid¹^ORCID,Shahabi Hejar²^ORCID,Piralilou Sepideh Tavakkoli³,Crivellari Alessandro⁴^ORCID,Rosa Laura Elena Cué la⁵^ORCID,Atzberger Clement¹,Li Jonathan⁶^ORCID,Ghamisi Pedram⁷^ORCID

Affiliation:

1. Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria

2. Center Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC, Canada

3. Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria

4. Department of Geography, National Taiwan University, Taipei, Taiwan

5. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands

6. Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada

7. Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany

Funder

National Taiwan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10646206.pdf?arnumber=10646206

Reference57 articles.

1. Estimating the quality of landslide susceptibility models

2. A review of landslide acceptable risk and tolerable risk

3. Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

4. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

5. MAST: An Earthquake-Triggered Landslides Extraction Method Combining Morphological Analysis Edge Recognition With Swin-Transformer Deep Learning Model