FloodNet-Lite: A Lightweight Deep Learning for Flood Mapping Using Remote Sensing Data With Optimized UNet and Edge Deployment Approach in 6G

FloodNet-Lite: A Lightweight Deep Learning for Flood Mapping Using Remote Sensing Data With Optimized UNet and Edge Deployment Approach in 6G

Flood mapping using remote sensing data is critical to disaster response, especially in real-time monitoring and edge deployment. However, existing deep-learning (DL) models often face challenges related to computational complexity, latency, and limited scalability in dynamic and resource-constraine...

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Bibliographic Details
Main Authors: Puviyarasi Thirugnanasammandamoorthi, Debabrata Ghosh, Ram Kishan Dewangan, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Huda Saleh Abbas, Hashim Elshafie, Rashid A. Saeed, Ala Eldin Awouda
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
6G
disaster management
edge computing
environmental monitoring
flood mapping
lightweight deep learning (DL)
Online Access:https://ieeexplore.ieee.org/document/11087685/
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