arXiv Machine Learning June 30, 2026 By Hooman Torkaman, Ellis Oti Boateng, Jignesh Solanki, Anurag Srivastava

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

Read the original on arXiv Machine Learning →

arXiv:2606. 29117v1 Announce Type: cross Abstract: Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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arXiv Machine Learning

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Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

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