MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation

YoonJe Kang, Jung Yonghoon, Wonseop Shin, Bumsoo Kim, Sanghyun Seo

Student Lead Author Indication: Yes

Keywords: Synthetic Dataset Generation, Flood Detection, Digital Twin

TL;DR: We propose a parameter-controllable synthetic dataset generation framework for flood situation detection with multi-type annotations.

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.

Submission Number: 10