构建多标注洪水合成数据集，提升灾害检测效果。
MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation
- 基于真实物理特性生成高保真洪水场景。
- 数据集含5级标注，支持多种下游任务。
- 合成数据在真实感与检测性能上媲美真实数据。
本文提出一种用于洪水灾害检测系统的合成数据生成框架。为保证高保真度与质量，我们将在真实世界中观测到的多种物理特性映射至虚拟环境，并通过控制这些特性来模拟洪水场景。为提高效率，借助图像到3D及城市合成领域的最新生成模型，快速组合洪水环境，避免手工设计带来的数据偏差。基于该框架，我们构建了包含5个层级标注的洪水合成数据集MultiFloodSynth，涵盖法线图、分割掩码、3D边界框等多种标注类型，适用于多种下游任务。实验表明，该数据集在保持与真实数据相当的真实感的同时，显著提升了洪水灾害检测性能。
原文摘要 · Abstract (English)
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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