MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation
This addresses data scarcity for flood hazard detection systems, though it is incremental as it builds on existing generative models.
The paper tackles the problem of limited training data for flood hazard detection systems by developing a synthetic data generation framework that simulates flood scenarios with high fidelity. The resulting MultiFloodSynth dataset demonstrates enhanced detection performance with realism comparable to real datasets.
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.