Automated Floodwater Depth Estimation Using Large Multimodal Model for
Rapid Flood Mapping
- URL: http://arxiv.org/abs/2402.16684v1
- Date: Mon, 26 Feb 2024 16:02:15 GMT
- Title: Automated Floodwater Depth Estimation Using Large Multimodal Model for
Rapid Flood Mapping
- Authors: Temitope Akinboyewa, Huan Ning, M. Naser Lessani, Zhenlong Li
- Abstract summary: This paper presents an automated and fast approach for estimating floodwater depth from on-site flood photos.
A pre-trained large multimodal model, GPT-4 Vision, was used specifically for estimating floodwater.
Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Information on the depth of floodwater is crucial for rapid mapping of areas
affected by floods. However, previous approaches for estimating floodwater
depth, including field surveys, remote sensing, and machine learning
techniques, can be time-consuming and resource-intensive. This paper presents
an automated and fast approach for estimating floodwater depth from on-site
flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used
specifically for estimating floodwater. The input data were flooding photos
that contained referenced objects, such as street signs, cars, people, and
buildings. Using the heights of the common objects as references, the model
returned the floodwater depth as the output. Results show that the proposed
approach can rapidly provide a consistent and reliable estimation of floodwater
depth from flood photos. Such rapid estimation is transformative in flood
inundation mapping and assessing the severity of the flood in near-real time,
which is essential for effective flood response strategies.
Related papers
- AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring [0.0]
Flooding is a major natural hazard causing significant fatalities and economic losses annually, with increasing frequency due to climate change.
This study compares the performance of three deep learning models UNet, ResNet, and DeepLabv3 for pixelwise water segmentation to aid in flood detection, utilizing images from drones, in field observations, and social media.
arXiv Detail & Related papers (2025-01-14T17:26:02Z) - Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion [51.69876947593144]
Existing methods for depth completion operate in tightly constrained settings.
Inspired by advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation.
Marigold-DC builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance.
arXiv Detail & Related papers (2024-12-18T00:06:41Z) - MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak
Inundation Depth And Decoding Influencing Features [0.8497188292342053]
This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County.
MaxFloodCast offers efficient and interpretable flood inundation depth predictions.
arXiv Detail & Related papers (2023-08-11T16:58:57Z) - Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
Flood inundation forecast provides critical information for emergency planning before and during flood events.
High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding.
We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
arXiv Detail & Related papers (2023-07-29T22:49:50Z) - 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) - Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN [60.257791714663725]
We propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes.
The proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.
arXiv Detail & Related papers (2022-12-23T03:00:28Z) - Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth
Mapping [1.6244541005112747]
In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs.
Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies.
arXiv Detail & Related papers (2022-09-06T18:16:12Z) - Feasibility study of urban flood mapping using traffic signs for route
optimization [9.973554387848257]
Water events are the most frequent and costliest climate disasters around the world.
In the U.S., an estimated 127 million people who live in coastal areas are at risk of substantial home damage from hurricanes or flooding.
arXiv Detail & Related papers (2021-09-24T02:13:23Z) - Underwater Image Restoration via Contrastive Learning and a Real-world
Dataset [59.35766392100753]
We present a novel method for underwater image restoration based on unsupervised image-to-image translation framework.
Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images.
arXiv Detail & Related papers (2021-06-20T16:06:26Z) - Progressive Depth Learning for Single Image Dehazing [56.71963910162241]
Existing dehazing methods often ignore the depth cues and fail in distant areas where heavier haze disturbs the visibility.
We propose a deep end-to-end model that iteratively estimates image depths and transmission maps.
Our approach benefits from explicitly modeling the inner relationship of image depth and transmission map, which is especially effective for distant hazy areas.
arXiv Detail & Related papers (2021-02-21T05:24:18Z) - Water level prediction from social media images with a multi-task
ranking approach [0.0]
We introduce a computer vision system that estimates water depth from social media images taken during flooding events.
We propose a multi-task learning approach, where a model is trained using both a regression and a pairwise ranking loss.
We show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with 11 cm root mean square error.
arXiv Detail & Related papers (2020-07-14T00:51:29Z)
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.