# MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation

arXiv: https://arxiv.org/abs/2502.03966 · HTML: https://mlsanity.com/paper/2502.03966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 17 of 17)

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- [PSP-HDRI+: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models](https://mlsanity.com/paper/2207.05025/) (2022)
- [Water level prediction from social media images with a multi-task ranking approach](https://mlsanity.com/paper/2007.06749/) (2020)
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- [Controllable Image Synthesis of Industrial Data Using Stable Diffusion](https://mlsanity.com/paper/2401.03152/) (2024)
- [Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment](https://mlsanity.com/paper/2311.11039/) (2023)
- [Deep Sensing of Urban Waterlogging](https://mlsanity.com/paper/2103.05927/) (2021)
- [Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model](https://mlsanity.com/paper/2410.18678/) (2024)

## Abstract (arXiv)

In this paper, we present synthetic data generation framework for flood hazard detection system. For high fidelity and quality, we characterize several real-world properties into virtual world and simulate the flood situation by controlling them. For the sake of efficiency, recent generative models in image-to-3D and urban city synthesis are leveraged to easily composite flood environments so that we avoid data bias due to the hand-crafted manner. Based on our framework, we build the flood synthetic dataset with 5 levels, dubbed MultiFloodSynth which contains rich annotation types like normal map, segmentation, 3D bounding box for a variety of downstream task. In experiments, our dataset demonstrate the enhanced performance of flood hazard detection with on-par realism compared with real dataset.