AdU-Net: Adaptive Dilated U-Net for Glacial Lake Region Detection and Outburst Risk Assessment Using Satellite Imagery

Summary

The paper presents AdU-Net, a framework that pairs a dilated U-Net with nested connections and an adaptable vision transformer encoder (AVi-TE) to extract glacial lakes from Landsat 8 imagery of the Imja, Chandra and Bhaga regions. A modified spiking neural network then analyzes the extracted lake features over time to model glacial lake outburst flood (GLOF) risk. The authors state that combining spatial and temporal analysis gives a more complete view of lake dynamics and the factors behind outburst susceptibility.