Application of Deep Learning Techniques in Water Level Measurement: Combining Improved SegFormer-UNet Model with Virtual Water Gauge

Xie, Zhifeng; Jianhui, Jin; Wang, Jianping; Zhang, Rongxing; Li, Shenghong

doi:10.3390/app13095614

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Jafari et al [58] used the fully convolutional network for segmentation, which was verified during Hurricane Harvey. Xie et al [59] used an improved SegFormer-UNet segmentation model to accurately segment the water body from a given image. However, semantic segmentation networks are often trained using supervised learning methods, which need the prior construction of a dataset.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Wu,

Huang,

Huang

et al. 2023

Water

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As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Wu,

Huang,

Huang

et al. 2023

Water

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“…This study aims to propose an efficient and accurate building extraction method for remote sensing images based on the SegFormer model [6,7]. Specifically, we will first prepare data to generate training data.…”

mentioning

confidence: 99%

Building Extraction on SegFormer Model and Post Processing from Remote Sensing Images

Li,

Wang,

Yan

2024

Preprint

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Most of the traditional methods are based on remote sensing feature classification technology, which uses different classification methods to extract specific feature types, but the traditional classification process suffers from the problems of high threshold of use, cumbersome data processing process, slow recognition speed, and poor migration. Artificial intelligence, especially machine learning and deep learning, is constantly and deeply affecting our daily life and work, and the impact on intelligent extraction of remote sensing images is also very extensive. Classification and automatic extraction of geographic elements of remote sensing images is a popular research direction in the field of remote sensing. Remote sensing image building extraction has an important application value in the field of geographic information, especially in urban planning, resource management, and ecological protection. Deep learning convolutional neural network is used to recognize buildings in remote sensing images, and the current mainstream SegFormer network structure is selected for intelligent binary classification to extract buildings. The intelligent binary classification workflow ranges from data preparation, model construction, model release to application. Intelligent binary classification can intelligently decipher not only buildings, but also single land classes with obvious feature points such as roads and rivers. The development of deep learning algorithms, to a certain extent, to make up for some of the shortcomings of the traditional remote sensing image analysis methods, post-processing of the inference results, it greatly improves the presentation of the results, as well as the accuracy of the results, to provide a better technical basis for the analysis of remote sensing images.

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Evaluation of deep learning computer vision for water level measurements in rivers

Liu,

Huang

2024

Heliyon

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Application of Deep Learning Techniques in Water Level Measurement: Combining Improved SegFormer-UNet Model with Virtual Water Gauge

Cited by 5 publications

References 21 publications

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Building Extraction on SegFormer Model and Post Processing from Remote Sensing Images

Evaluation of deep learning computer vision for water level measurements in rivers

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