Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training
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| Udgivet i: | Hydrology vol. 12, no. 11 (2025), p. 307-328 |
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| Andre forfattere: | , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3275518944 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5338 | ||
| 024 | 7 | |a 10.3390/hydrology12110307 |2 doi | |
| 035 | |a 3275518944 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Lin, Lin |u School of Water Conservancy and Transportation, Zhengzhou University, No. 100 Science Rd, Zhengzhou 450001, China; linlin577@zzu.edu.cn | |
| 245 | 1 | |a Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications. | |
| 653 | |a Feature extraction | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Water depth | ||
| 653 | |a Urban hydrology | ||
| 653 | |a Data integration | ||
| 653 | |a Moisture content | ||
| 653 | |a Annotations | ||
| 653 | |a Space perception | ||
| 653 | |a Disasters | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Contrast media | ||
| 653 | |a Training | ||
| 653 | |a Accuracy | ||
| 653 | |a Hydrology | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Vectors | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Real time | ||
| 653 | |a Scale models | ||
| 653 | |a Water content | ||
| 653 | |a Water engineering | ||
| 653 | |a Big Data | ||
| 653 | |a Social networks | ||
| 653 | |a Floods | ||
| 653 | |a Floodwater | ||
| 653 | |a Social media | ||
| 653 | |a Depth perception | ||
| 653 | |a Monitoring | ||
| 653 | |a Urban areas | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Physical properties | ||
| 653 | |a Social resilience | ||
| 653 | |a Image quality | ||
| 653 | |a Object recognition | ||
| 653 | |a Estimation | ||
| 653 | |a Digital media | ||
| 700 | 1 | |a Zeng Zhenli |u School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, No. 1037 Luoyu Rd, Wuhan 430074, China; m202573784@hust.edu.cn (Z.Z.); m202573965@hust.edu.cn (Y.X.) | |
| 700 | 1 | |a Tang Chaoqing |u School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, No. 1037 Luoyu Rd, Wuhan 430074, China; m202573784@hust.edu.cn (Z.Z.); m202573965@hust.edu.cn (Y.X.) | |
| 700 | 1 | |a Xie Yilin |u School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, No. 1037 Luoyu Rd, Wuhan 430074, China; m202573784@hust.edu.cn (Z.Z.); m202573965@hust.edu.cn (Y.X.) | |
| 700 | 1 | |a Liang Qiuhua |u School of Water Conservancy and Transportation, Zhengzhou University, No. 100 Science Rd, Zhengzhou 450001, China; linlin577@zzu.edu.cn | |
| 773 | 0 | |t Hydrology |g vol. 12, no. 11 (2025), p. 307-328 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275518944/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275518944/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275518944/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |