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Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the novel MRGSLD Dataset.

Domaine:

geospatialenvironment and energy

Type de record:

datasetpaper
Créateur:
Emani, Ghislain Franck
Éditeur:
Zenodo
Hôte:avatar

This repository contains implementations details about my publications concerning 

Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the novel MRGSLD Dataset.


-A novel global large-scale dataset MRGSLD containing 21 distinct regions (in some data in Africa and Europe regions ) distributed around the globe is created. 
- An innovative Multiple Fusion Synthetic Minority Oversampling technique for landslide data augmentation is advised to address the dataset imbalance.
-The MSFAM-ResAttUnet network for landslide segmentation is proposed.
-The feature attention module (FAM) was proposed to avoid the loss of landslide informations during the downsampling and helps the model ignore noisy pixels. While the Multi-Spectral (MS) branch was added to the network to leverage spectral features contained in the remote sensing images (RSI)

Visit

doi.org

Tasks

computer visionimage classification

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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