Physics-informed GANs for Coastal Flood Visualization
- URL: http://arxiv.org/abs/2010.08103v2
- Date: Fri, 12 Feb 2021 06:26:46 GMT
- Title: Physics-informed GANs for Coastal Flood Visualization
- Authors: Bj\"orn L\"utjens, Brandon Leshchinskiy, Christian Requena-Mesa,
Farrukh Chishtie, Natalia D\'iaz-Rodriguez, Oc\'eane Boulais, Aaron Pi\~na,
Dava Newman, Alexander Lavin, Yarin Gal, Chedy Ra\"issi
- Abstract summary: We create a deep learning pipeline that generates visual satellite images of current and future coastal flooding.
By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism.
While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.
- Score: 65.54626149826066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As climate change increases the intensity of natural disasters, society needs
better tools for adaptation. Floods, for example, are the most frequent natural
disaster, but during hurricanes the area is largely covered by clouds and
emergency managers must rely on nonintuitive flood visualizations for mission
planning. To assist these emergency managers, we have created a deep learning
pipeline that generates visual satellite images of current and future coastal
flooding. We advanced a state-of-the-art GAN called pix2pixHD, such that it
produces imagery that is physically-consistent with the output of an
expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery
relative to physics-based flood maps, we find that our proposed framework
outperforms baseline models in both physical-consistency and photorealism.
While this work focused on the visualization of coastal floods, we envision the
creation of a global visualization of how climate change will shape our earth.
Related papers
- Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model [17.016411785224317]
We present DAVI (Disaster Assessment with VIsion foundation model), which overcomes domain disparities and detects structural damage without requiring ground-truth labels of the target region.
DAVI integrates task-specific knowledge from a model trained on source regions with an image segmentation foundation model to generate pseudo labels of possible damage in the target region.
It then employs a two-stage refinement process, targeting both the pixel and overall image, to more accurately pinpoint changes in disaster-struck areas.
arXiv Detail & Related papers (2024-06-12T09:21:28Z) - Typhoon Intensity Prediction with Vision Transformer [51.84456610977905]
We introduce "Typhoon Intensity Transformer" (Tint) to predict typhoon intensity accurately across space and time.
Tint uses self-attention mechanisms with global receptive fields per layer.
Experiments on a publicly available typhoon benchmark validate the efficacy of Tint.
arXiv Detail & Related papers (2023-11-28T03:11:33Z) - An evaluation of deep learning models for predicting water depth
evolution in urban floods [59.31940764426359]
We compare different deep learning models for prediction of water depth at high spatial resolution.
Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model.
Our results show that the deep learning models present in general lower errors compared to the other methods.
arXiv Detail & Related papers (2023-02-20T16:08:54Z) - Transformer-based Flood Scene Segmentation for Developing Countries [1.7499351967216341]
Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil.
Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage.
FloodTransformer is the first visual transformer-based model to detect and segment flooded areas from aerial images at disaster sites.
arXiv Detail & Related papers (2022-10-09T10:29:41Z) - Learning to restore images degraded by atmospheric turbulence using
uncertainty [93.72048616001064]
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems.
We propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence.
arXiv Detail & Related papers (2022-07-07T17:24:52Z) - ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods [89.61670857155173]
We present our solution to simulate photo-realistic floods on authentic images.
We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
arXiv Detail & Related papers (2021-10-06T15:54:57Z) - Physically-Consistent Generative Adversarial Networks for Coastal Flood
Visualization [60.690929022840685]
We propose the first deep learning pipeline to ensure physical-consistency in synthetic visual satellite imagery.
By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism.
We publish a dataset of over 25k labelled image-pairs to study image-to-image translation in Earth observation.
arXiv Detail & Related papers (2021-04-10T15:00:15Z) - Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation
Features [0.2538209532048866]
We propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane.
The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017.
In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster.
arXiv Detail & Related papers (2020-12-15T21:30:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.