# RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images

arXiv: https://arxiv.org/abs/2507.11143 · HTML: https://mlsanity.com/paper/2507.11143/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [Boundary Loss for Remote Sensing Imagery Semantic Segmentation](https://mlsanity.com/paper/1905.07852/) (2019)
- [Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection](https://mlsanity.com/paper/2206.00515/) (2022)
- [The Lov'asz Hinge: A Novel Convex Surrogate for Submodular Losses](https://mlsanity.com/paper/1512.07797/) (2015)
- [The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery](https://mlsanity.com/paper/2209.02556/) (2022)
- [SAR-based landslide classification pretraining leads to better segmentation](https://mlsanity.com/paper/2211.09927/) (2022)
- [Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network](https://mlsanity.com/paper/2312.16717/) (2023)
- [Relict landslide detection using Deep-Learning architectures for image segmentation in rainforest areas: A new framework](https://mlsanity.com/paper/2208.02693/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image](https://mlsanity.com/paper/2507.12939/) (2025)
- [Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data](https://mlsanity.com/paper/2605.09746/) (2026)

## Abstract (arXiv)

In recent years, landslide disasters have reported frequently due to the extreme weather events of droughts, floods , storms, or the consequence of human activities such as deforestation, excessive exploitation of natural resources. However, automatically observing landslide is challenging due to the extremely large observing area and the rugged topography such as mountain or highland. This motivates us to propose an end-to-end deep-learning-based model which explores the remote sensing images for automatically observing landslide events. By considering remote sensing images as the input data, we can obtain free resource, observe large and rough terrains by time. To explore the remote sensing images, we proposed a novel neural network architecture which is for two tasks of landslide detection and landslide segmentation. We evaluated our proposed model on three different benchmark datasets of LandSlide4Sense, Bijie, and Nepal. By conducting extensive experiments, we achieve F1 scores of 98.23, 93.83 for the landslide detection task on LandSlide4Sense, Bijie datasets; mIoU scores of 63.74, 76.88 on the segmentation tasks regarding LandSlide4Sense, Nepal datasets. These experimental results prove potential to integrate our proposed model into real-life landslide observation systems.