Semi-supervised identification and mapping of surface water extent using street-level monitoring videos
Urban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-11-01
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| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2022.2123352 |
| _version_ | 1798017979691040768 |
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| author | Ruo-Qian Wang Yangmin Ding |
| author_facet | Ruo-Qian Wang Yangmin Ding |
| author_sort | Ruo-Qian Wang |
| collection | DOAJ |
| description | Urban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water ponding, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrate the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study have a great potential to study other street-level and earth surface processes. |
| first_indexed | 2024-04-11T16:16:26Z |
| format | Article |
| id | doaj.art-60e13622b6d04ac388a77aee5f8c4e9a |
| institution | Directory Open Access Journal |
| issn | 2096-4471 2574-5417 |
| language | English |
| last_indexed | 2024-04-11T16:16:26Z |
| publishDate | 2022-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj.art-60e13622b6d04ac388a77aee5f8c4e9a2022-12-22T04:14:30ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172022-11-0111910.1080/20964471.2022.2123352Semi-supervised identification and mapping of surface water extent using street-level monitoring videosRuo-Qian Wang0Yangmin Ding1Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USANEC Laboratories America Inc, 4 Independence Way, Princeton, NJ, USAUrban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water ponding, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrate the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study have a great potential to study other street-level and earth surface processes.https://www.tandfonline.com/doi/10.1080/20964471.2022.2123352Segmentationdeep learningmonoplottingsmart citymonocular visual data |
| spellingShingle | Ruo-Qian Wang Yangmin Ding Semi-supervised identification and mapping of surface water extent using street-level monitoring videos Big Earth Data Segmentation deep learning monoplotting smart city monocular visual data |
| title | Semi-supervised identification and mapping of surface water extent using street-level monitoring videos |
| title_full | Semi-supervised identification and mapping of surface water extent using street-level monitoring videos |
| title_fullStr | Semi-supervised identification and mapping of surface water extent using street-level monitoring videos |
| title_full_unstemmed | Semi-supervised identification and mapping of surface water extent using street-level monitoring videos |
| title_short | Semi-supervised identification and mapping of surface water extent using street-level monitoring videos |
| title_sort | semi supervised identification and mapping of surface water extent using street level monitoring videos |
| topic | Segmentation deep learning monoplotting smart city monocular visual data |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2022.2123352 |
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