Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning
Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6753 |
| _version_ | 1797586058049748992 |
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| author | Jiageng Zhong Ming Li Hanqi Zhang Jiangying Qin |
| author_facet | Jiageng Zhong Ming Li Hanqi Zhang Jiangying Qin |
| author_sort | Jiageng Zhong |
| collection | DOAJ |
| description | Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution. |
| first_indexed | 2024-03-11T00:17:53Z |
| format | Article |
| id | doaj.art-e061b05ae3ff4bed916340380c73c1ab |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:53Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e061b05ae3ff4bed916340380c73c1ab2023-11-18T23:34:01ZengMDPI AGSensors1424-82202023-07-012315675310.3390/s23156753Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine LearningJiageng Zhong0Ming Li1Hanqi Zhang2Jiangying Qin3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, ChinaCorals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution.https://www.mdpi.com/1424-8220/23/15/6753underwater photogrammetrydeep learningsemantic segmentationcoral reefs3D analysis |
| spellingShingle | Jiageng Zhong Ming Li Hanqi Zhang Jiangying Qin Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning Sensors underwater photogrammetry deep learning semantic segmentation coral reefs 3D analysis |
| title | Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning |
| title_full | Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning |
| title_fullStr | Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning |
| title_full_unstemmed | Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning |
| title_short | Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning |
| title_sort | fine grained 3d modeling and semantic mapping of coral reefs using photogrammetric computer vision and machine learning |
| topic | underwater photogrammetry deep learning semantic segmentation coral reefs 3D analysis |
| url | https://www.mdpi.com/1424-8220/23/15/6753 |
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