Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data
Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/20/4955 |
| _version_ | 1797572380547088384 |
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| author | Zhongqiang Wu Shulei Wu Haixia Yang Zhihua Mao Wei Shen |
| author_facet | Zhongqiang Wu Shulei Wu Haixia Yang Zhihua Mao Wei Shen |
| author_sort | Zhongqiang Wu |
| collection | DOAJ |
| description | Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing. |
| first_indexed | 2024-03-10T20:55:27Z |
| format | Article |
| id | doaj.art-354883a4534d4841a23954d062ca872a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:55:27Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-354883a4534d4841a23954d062ca872a2023-11-19T17:58:46ZengMDPI AGRemote Sensing2072-42922023-10-011520495510.3390/rs15204955Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 DataZhongqiang Wu0Shulei Wu1Haixia Yang2Zhihua Mao3Wei Shen4School of Information Science and Technology, Hainan Normal University, Haikou 571158, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou 571158, ChinaChina Siwei Surveying and Mapping Technology Co., Ltd., Beijing 100048, ChinaStates Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, ChinaSchool of Marine Science, Shanghai Ocean University, Shanghai 201306, ChinaWater depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing.https://www.mdpi.com/2072-4292/15/20/4955big modelmachine learningBaidu Easy-DLwater depthsatellite-based bathymetry |
| spellingShingle | Zhongqiang Wu Shulei Wu Haixia Yang Zhihua Mao Wei Shen Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data Remote Sensing big model machine learning Baidu Easy-DL water depth satellite-based bathymetry |
| title | Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data |
| title_full | Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data |
| title_fullStr | Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data |
| title_full_unstemmed | Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data |
| title_short | Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data |
| title_sort | enhancing water depth estimation from satellite images using online machine learning a case study using baidu easy dl with acoustic bathymetry and sentinel 2 data |
| topic | big model machine learning Baidu Easy-DL water depth satellite-based bathymetry |
| url | https://www.mdpi.com/2072-4292/15/20/4955 |
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