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

arXiv: https://arxiv.org/abs/2405.14232 · HTML: https://mlsanity.com/paper/2405.14232/

## 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 (most cited first, 9 of 9)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Feature Space Augmentation for Long-Tailed Data](https://mlsanity.com/paper/2008.03673/) (2020)
- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data](https://mlsanity.com/paper/2006.09201/) (2020)
- [Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas](https://mlsanity.com/paper/2309.14610/) (2023)
- [Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features](https://mlsanity.com/paper/2311.01661/) (2023)
- [Relevancy Classification of Multimodal Social Media Streams for Emergency Services](https://mlsanity.com/paper/1907.07240/) (2019)

## Built on by (most cited first, 4 of 4)

- [Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery](https://mlsanity.com/paper/2506.06667/) (2025)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones](https://mlsanity.com/paper/2606.29134/) (2026)
- [Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcasting](https://mlsanity.com/paper/2602.10312/) (2026)

## 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.