Yi He

InSAR and Deep Learning in Landslides Research: Intelligent Identification, Risk Assessment and Susceptibility Mapping (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book combines remote sensing and deep learning technology to develop a variety of models in the study of different type landslides in a wide range of areas including northwest, southwest and southern China. It explores the application of various deep learning methods in landslide identification and sensitivity mapping. It also explores intelligent landslide monitoring and susceptibility mapping using a variety of data and methods, providing ideas and methods for landslide prevention and mitigation. This book is suitable for professionals in the field of landslide monitoring and graduate students in the fields of remote sensing and geological hazards research to mitigate this most widespread and harmful geological hazards in the world.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 214
• Erscheinungstermin: 23. Juli 2025
• Englisch
• ISBN-13: 9789819691326
• Artikelnr.: 74987072

Autorenporträt

Dr. Yi He received his B.S. degrees in Geographic Information System from Lanzhou Jiaotong University, Lanzhou, China, in 2011, and the Ph.D. degree in Geography from the Lanzhou University, Lanzhou, in 2016. Since Jan. 2023, Dr. He has been in Faculty of Geomatics, Lanzhou Jiaotong University as a professor. Dr. He focuses on the research of landslide based on remote sensing technology and deep learning. His recent research interests include: deep learning landslide identification and sensitivity mapping, InSAR technology ground surface deformation monitoring and time series prediction, and key technologies of InSAR data processing based on deep learning, etc. He has presided over many national and provincial projects, including the National Natural Science Foundation of China General Project, the National Natural Science Foundation of China Youth Science Fund, the China Postdoctoral General Fund, and other important projects.

Inhaltsangabe

Introduction.- InSAR and deep learning theory.- Deep learning landslide intelligent identification methods.- Landslide susceptibility assessment based on geography consistency constraints.- Landslide susceptibility assessment by integrated multi-model based on static-dynamic data.- Landslide susceptibility assessment based on integrated static-dynamic characteristics of InSAR deformation information.