What is it about?

Deep learning models are used to classify satellite images of the same location, captured at different times, to detect landslides. This work is carried out in the mountainous Himalayan region of Nepal.

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Why is it important?

Landslides are mass movements of the earth surface occurring on hill slopes. They pose serious threats to life and property. Mountainous regions that satisfy certain precursors to mass movement of the earth surface are hotspot zones for landslides. A quick inventory generation of landslides that occur in these regions are important for rapid response to landslide disasters.


Traditional methods for landslide inventory mapping from satellite image interpretation have complex and time-consuming workflows; however, deep learning techniques make the work a lot more easier. What's more, the introduction of attention mechanisms into deep neural network architectures boost model performance as demonstrated in this work.

Mr. Solomon Obiri Yeboah Amankwah
Nanjing University of Information Science and Technology

Read the Original

This page is a summary of: Landslide detection from bitemporal satellite imagery using attention-based deep neural networks, Landslides, June 2022, Springer Science + Business Media,
DOI: 10.1007/s10346-022-01915-6.
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