# Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection

arXiv: https://arxiv.org/abs/2607.15527 · HTML: https://mlsanity.com/paper/2607.15527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 33 of its 41 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler](https://mlsanity.com/paper/2502.20110/) (2025)
- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [UniK3D: Universal Camera Monocular 3D Estimation](https://mlsanity.com/paper/2503.16591/) (2025)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field](https://mlsanity.com/paper/2211.13226/) (2022)
- [Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles](https://mlsanity.com/paper/1906.08716/) (2019)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [RAD: Region-Aware Diffusion Models for Image Inpainting](https://mlsanity.com/paper/2412.09191/) (2024)
- [Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets](https://mlsanity.com/paper/2512.15110/) (2025)
- [ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods](https://mlsanity.com/paper/2110.02871/) (2021)
- [WeatherEdit: Controllable Weather Editing with 4D Gaussian Field](https://mlsanity.com/paper/2505.20471/) (2025)
- [GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment](https://mlsanity.com/paper/2604.25370/) (2026)
- [FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion](https://mlsanity.com/paper/2403.03463/) (2024)
- [Using Simulated Data to Generate Images of Climate Change](https://mlsanity.com/paper/2001.09531/) (2020)

## Abstract (arXiv)

Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and the development of anomaly detection models. However, the severe shortage of flood data in real-world environments, combined with the inherent distortions of fisheye lens images, which are used for urban surveillance, has made high-precision simulations challenging. To address this, we propose a new physical simulation system PhysFlood that leverages Diffusion Models to synthesize realistic floods from just a single image captured by a fisheye lens. Our system not only enables simulation from a single image, but also features the ability to freely control and generate diverse flood scenarios by manipulating physically meaningful variables, such as water levels. In our evaluation experiments, we conducted a qualitative human study and demonstrated that the simulation images generated by PhysFlood exhibit both acceptable realism and robustness.