MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation

• URL: http://arxiv.org/abs/2502.03966v3
• Date: Thu, 13 Feb 2025 08:54:42 GMT
• Title: MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation
• Authors: YoonJe Kang, Yonghoon Jung, Wonseop Shin, Bumsoo Kim, Sanghyun Seo,
• Abstract summary: We characterize several real-world properties into virtual world and simulate the flood situation by controlling them. Based on our framework, we build the flood synthetic dataset with 5 levels, dubbed MultiFlood Synth. In experiments, our dataset demonstrate the enhanced performance of flood hazard detection with on-par realism compared with real dataset.
• Score: 5.431779602239565
• License:
• Abstract: 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.

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