mlsanityData to 14 Oct 2026

FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation

arXiv 2405.14232 ↗First posted 23 May 2024cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed decisions regarding evacuation orders and infrastructure repair priorities during disaster response and recovery. Here, we introduce FloodDamageCast, a machine learning framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data to predict residential flood damage at a resolution of 500 meters by 500 meters within Harris County, Texas, during the 2017 Hurricane Harvey. To deal with data imbalance, FloodDamageCast incorporates a generative adversarial networks-based data augmentation coupled with an efficient machine learning model. The results demonstrate the model's ability to identify high-damage spatial areas that would be overlooked by baseline models. Insights gleaned from flood damage nowcasting can assist emergency responders to more efficiently identify repair needs, allocate resources, and streamline on-the-ground inspections, thereby saving both time and effort.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 63 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

9 of 63 references.

References · 9

  1. 2019Modeling Tabular data using Conditional GANcited by 699
  2. 2015Cost Sensitive Learning of Deep Feature Representations from Imbalanced Datacited by 167
  3. 2021Delving into Deep Imbalanced Regressioncited by 158
  4. 2020Feature Space Augmentation for Long-Tailed Datacited by 136
  5. 2021A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Featurescited by 13
  6. 2020A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Datacited by 10
  7. 2023Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areascited by 6
  8. 2023Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Featurescited by 4
  9. 2019Relevancy Classification of Multimodal Social Media Streams for Emergency Servicescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagerycited by 3
  2. 2026CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessmentcited by 3
  3. 2026Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbonescited by 1
  4. 2026Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcastingcited by 0