Cross-Architecture Foundation-Model Distillation for Edge Flood Segmentation
cs.CV, cs.LG
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: Main paper (17 pages) with supplementary material (11 pages). Submitted to IEEE JSTARS, Special Section on Generalist-Specialist Model Synergy for Remote Sensing: Theories, Methods, and Applications
Code: https://github.com/sycz00/FloodDistill
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Flood Mapping from RGB imagery using a Vision Foundation Model
- Distilling the Knowledge in a Neural Network
- FitNets: Hints for Thin Deep Nets
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
- InstaGeo: Compute-Efficient Geospatial Machine Learning from Data to Deployment
- Active Learning for Convolutional Neural Networks: A Core-Set Approach
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