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.
@article{arxiv.2502.03966,
title = {MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation},
author = {YoonJe Kang and Yonghoon Jung and Wonseop Shin and Bumsoo Kim and Sanghyun Seo},
journal= {arXiv preprint arXiv:2502.03966},
year = {2025}
}
Comments
6 pages, 6 figures. Accepted as Oral Presentation to AAAI 2025 Workshop on Good-Data