Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data
Abstract Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovati...
Main Authors: | Kushanav Bhuyan, Hakan Tanyaş, Lorenzo Nava, Silvia Puliero, Sansar Raj Meena, Mario Floris, Cees van Westen, Filippo Catani |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-27352-y |
Similar Items
-
Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model
by: Kushanav Bhuyan, et al.
Published: (2023-12-01) -
Rapid Mapping of Landslides on SAR Data by Attention U-Net
by: Lorenzo Nava, et al.
Published: (2022-03-01) -
HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery
by: S. R. Meena, et al.
Published: (2023-07-01) -
Landslide topology uncovers failure movements
by: Kushanav Bhuyan, et al.
Published: (2024-03-01) -
An updating of landslide susceptibility prediction from the perspective of space and time
by: Zhilu Chang, et al.
Published: (2023-09-01)