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60.1 MB
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F410AB2E45ECA27085B2CC7B953CE9615EC70670
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June 2, 2025, 12:33 p.m.
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(Last updated: June 5, 2025, 3:02 p.m.)
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60.1 MB
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2025-06-02
| Uploaded by andryold1 | Size 60.1 MB | Health [ 37 /48 ] | Added 2025-06-02 |
NOTE
SOURCE: Sandric I. Earth Observation Applications to Landslide Mapping,...Modeling 2024
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MEDIAINFO
Textbook in PDF format Description Earth Observation Applications to Landslide Mapping, Monitoring and Modeling: Cutting-edge Approacheswith Artificial Intelligence, Aerial and Satellite Imagery focuses on the application of drone and satellite imagery for landslide mapping, monitoring, and modeling. The topics covered include the use of ultrahigh spatial resolution imagery acquired by UAVs (Unmanned Aerial Vehicles) for mapping and predicting landslide activity, the use of satellite imagery for monitoring landslide activity, the assimilation of EO (EarthObservation) data into landslide susceptibility and hazard prediction models, and the building of landslide inventories. The primary objective of this book is the advancement of the scientific understanding and application of technologies to address a variety of areas related to landslide mapping and monitoring for robust and sustainable development. Earth Observation Applications to Landslide Mapping, Monitoring and Modeling be useful for PhD students, postdoctoral researchers, professors, and scientists in geoscience. Key Features Recent developments in landslide monitoring and mapping algorithms Provides clear and detailed case studies and methods that can be easily replicated and implemented in landslide monitoring systems Provides detailed methods for applying research to landslide monitoring and mapping TOC Title page Copyright Contents List of contributors Section 1: Introduction Section 2: Satellite data in landslide mapping and monitoring Section 3: Drone applications for landslide mapping and monitoring Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment Section 5: Future challenges and future outlook Index
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