Landslide detection from bitemporal satellite imagery using attention-based deep neural networks

Solomon Obiri Yeboah Amankwah, Guojie Wang, Kaushal Gnyawali, Daniel Fiifi Tawiah Hagan, Isaac Sarfo, Dong Zhen, Isaac Kwesi Nooni, Waheed Ullah, Zheng Duan

Research output: Contribution to journalArticlepeer-review

Abstract

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides.

Original languageEnglish
Pages (from-to)2459-2471
Number of pages13
JournalLandslides
Volume19
Issue number10
DOIs
Publication statusPublished - 2022 Oct

Subject classification (UKÄ)

  • Other Earth and Related Environmental Sciences

Free keywords

  • Attention module
  • Change detection
  • Deep neural network (DNN)
  • Landslide mapping

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