3 papers
cs.IR2023
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
Tejit Pabari, Beth Tellman, Giannis Karamanolakis +11
Floods cause large losses to property, life, and livelihoods across the world every year, hindering sustainable development. Safety nets to help absorb financial shocks in disaster…
cs.CV2023
Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
Jonathan Giezendanner, Rohit Mukherjee, Matthew Purri +4
Mapping floods using satellite data is crucial for managing and mitigating flood risks. Satellite imagery enables rapid and accurate analysis of large areas, providing critical inf…
cs.CV2020
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
Peri Akiva, Matthew Purri, Kristin Dana +2
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information.…