FloodLense: A Framework for ChatGPT-based Real-time Flood Detection
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914655930155008 |
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| author | Kumbam, Pranath Reddy Vejre, Kshitij Maruti |
| author_facet | Kumbam, Pranath Reddy Vejre, Kshitij Maruti |
| contents | This study addresses the vital issue of real-time flood detection and management. It innovatively combines advanced deep learning models with Large language models (LLM), enhancing flood monitoring and response capabilities. This approach addresses the limitations of current methods by offering a more accurate, versatile, user-friendly and accessible solution. The integration of UNet, RDN, and ViT models with natural language processing significantly improves flood area detection in diverse environments, including using aerial and satellite imagery. The experimental evaluation demonstrates the models' efficacy in accurately identifying and mapping flood zones, showcasing the project's potential in transforming environmental monitoring and disaster management fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15501 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FloodLense: A Framework for ChatGPT-based Real-time Flood Detection Kumbam, Pranath Reddy Vejre, Kshitij Maruti Computer Vision and Pattern Recognition This study addresses the vital issue of real-time flood detection and management. It innovatively combines advanced deep learning models with Large language models (LLM), enhancing flood monitoring and response capabilities. This approach addresses the limitations of current methods by offering a more accurate, versatile, user-friendly and accessible solution. The integration of UNet, RDN, and ViT models with natural language processing significantly improves flood area detection in diverse environments, including using aerial and satellite imagery. The experimental evaluation demonstrates the models' efficacy in accurately identifying and mapping flood zones, showcasing the project's potential in transforming environmental monitoring and disaster management fields. |
| title | FloodLense: A Framework for ChatGPT-based Real-time Flood Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.15501 |