Landslide mapping with remote sensing: challenges and opportunities-Reference-Cited by-同舟云学术

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Landslide mapping with remote sensing: challenges and opportunities

Published:2019-10-15 Issue:4 Volume:41 Page:1555-1581
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zhong Cheng¹^ORCID,Liu Yue¹,Gao Peng²^ORCID,Chen Wenlong¹,Li Hui³^ORCID,Hou Yong¹,Nuremanguli Tuohuti³,Ma Haijian⁴

Affiliation:

1. Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences, Wuhan, China

2. Department of Geography, University of South Carolina, Columbia, South Carolina, USA

3. School of Earth Science, China University of Geosciences, Wuhan, China

4. Development Research Centre, China Earthquake Administration, Beijing, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1672904

Reference104 articles.

1. Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)

2. A brief survey of GIS in mass-movement studies, with reflections on theory and methods

3. Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea Region, Turkey)

4. Landslide Catastrophes and Disaster Risk Reduction: A GIS Framework for Landslide Prevention and Management

5. Application of infrared thermography for mapping open fractures in deep-seated rockslides and unstable cliffs

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1. Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review;Structural Engineering International;2024-09-12

2. ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data;Landslides;2024-09-09

3. Dynahead-YOLO-Otsu: an efficient DCNN-based landslide semantic segmentation method using remote sensing images;Geomatics, Natural Hazards and Risk;2024-09-02

4. Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion;Remote Sensing;2024-08-23

5. Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach;PeerJ Computer Science;2024-08-12

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