MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation

TL;DR

This paper introduces MultiFloodSynth, a synthetic dataset for flood hazard detection that combines real-world properties with generative models to improve detection accuracy and realism.

Contribution

We develop a novel synthetic data generation framework that leverages generative models for high-quality flood environment simulation with rich annotations.

Findings

Enhanced flood hazard detection performance using the dataset

Rich annotations support multiple downstream tasks

Synthetic data achieves realism comparable to real datasets

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.