Zobrazeno 1 - 10
of 326
pro vyhledávání: '"landslide mapping"'
Publikováno v:
Remote Sensing, Vol 16, Iss 19, p 3653 (2024)
Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development
Externí odkaz:
https://doaj.org/article/6219651f70bf4778a6039dcd44d2c252
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 126, Iss , Pp 103612- (2024)
Landslides frequently cause serious property damage and casualties. Therefore, it is crucial to have rapid and accurate landslide mapping (LM) to support post-earthquake landslide damage assessment and emergency rescue efforts. Many studies have been
Externí odkaz:
https://doaj.org/article/badcdd2fdee44207a1944a584ecac584
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3599-3610 (2023)
Landslide mapping (LM) from bitemporal remote sensing images is essential for disaster prevention and mitigation. Although bitemporal change detection technology has been applied for LM, there remains room for improvement in its accuracy and automati
Externí odkaz:
https://doaj.org/article/ad8684d3259a40ba8bb320a4de0c1002
Publikováno v:
Applied Sciences, Vol 14, Iss 9, p 3622 (2024)
The largest and the deepest landslides in Serbia occurred on the right valley side of the Danube. General conclusions about landslides along the Danube were obtained on the basis of their comprehensive, detailed investigations: the Sloboda bridge in
Externí odkaz:
https://doaj.org/article/edfb18fe22864796817e0e6d0801082e
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1344 (2024)
This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize deep learning tasks into five key frameworks—classifi
Externí odkaz:
https://doaj.org/article/9df02793f6174279aa5adaacbbc95e65
Akademický článek
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Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035008 (2024)
Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In r
Externí odkaz:
https://doaj.org/article/9fcd1a39f0bd49d18032c5b041f263ba
Publikováno v:
Big Earth Data, Vol 0, Iss 0, Pp 1-26 (2022)
Landslide detection is a hot topic in the remote sensing community, particularly with the current rapid growth in volume (and variety) of Earth observation data and the substantial progress of computer vision. Deep learning algorithms, especially ful
Externí odkaz:
https://doaj.org/article/eed90fc5fc6d41e39514ac5ea1781d62
Autor:
F. Bucci, M. Santangelo, F. Fiorucci, F. Ardizzone, D. Giordan, M. Cignetti, D. Notti, P. Allasia, D. Godone, D. Lagomarsino, A. Pozzoli, E. Norelli, M. Cardinali
Publikováno v:
Journal of Maps, Vol 17, Iss 2, Pp 376-388 (2021)
Landslide inventories provide the knowledge basis for many geomorphological applications and also planning and emergency management. Detailed landslide inventories should also be prepared where pre-existing inventories are available, as knowledge upd
Externí odkaz:
https://doaj.org/article/1e8b143adee944aab43b1df46238fb39
Editorial: Developments of remote sensing and numerical modeling applications for landslide analysis
Publikováno v:
Frontiers in Earth Science, Vol 10 (2023)
Externí odkaz:
https://doaj.org/article/9dd52b9291824071b09e6d400e6cdac9