An Overview of Coastline Extraction from Remote Sensing Data
The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing techno...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/19/4865 |
| _version_ | 1827722246208094208 |
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| author | Xixuan Zhou Jinyu Wang Fengjie Zheng Haoyu Wang Haitao Yang |
| author_facet | Xixuan Zhou Jinyu Wang Fengjie Zheng Haoyu Wang Haitao Yang |
| author_sort | Xixuan Zhou |
| collection | DOAJ |
| description | The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we propose the idea of extracting coastlines based on the coastline scene knowledge map (CSKG) semantic segmentation method. This review serves as a comprehensive reference for future development and research pertaining to coastal exploitation and management. |
| first_indexed | 2024-03-10T21:35:14Z |
| format | Article |
| id | doaj.art-0655891f008a4b10a30c173d727ee263 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T21:35:14Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0655891f008a4b10a30c173d727ee2632023-11-19T15:01:03ZengMDPI AGRemote Sensing2072-42922023-10-011519486510.3390/rs15194865An Overview of Coastline Extraction from Remote Sensing DataXixuan Zhou0Jinyu Wang1Fengjie Zheng2Haoyu Wang3Haitao Yang4Department of Graduate Management, Space Engineering University, Beijing 101400, ChinaDepartment of Graduate Management, Space Engineering University, Beijing 101400, ChinaDepartment of Aerospace Information, Space Engineering University, Beijing 101400, ChinaDepartment of Graduate Management, Space Engineering University, Beijing 101400, ChinaDepartment of Aerospace Information, Space Engineering University, Beijing 101400, ChinaThe coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we propose the idea of extracting coastlines based on the coastline scene knowledge map (CSKG) semantic segmentation method. This review serves as a comprehensive reference for future development and research pertaining to coastal exploitation and management.https://www.mdpi.com/2072-4292/15/19/4865coastline extractionremote sensingdeep learningremote sensing knowledge map |
| spellingShingle | Xixuan Zhou Jinyu Wang Fengjie Zheng Haoyu Wang Haitao Yang An Overview of Coastline Extraction from Remote Sensing Data Remote Sensing coastline extraction remote sensing deep learning remote sensing knowledge map |
| title | An Overview of Coastline Extraction from Remote Sensing Data |
| title_full | An Overview of Coastline Extraction from Remote Sensing Data |
| title_fullStr | An Overview of Coastline Extraction from Remote Sensing Data |
| title_full_unstemmed | An Overview of Coastline Extraction from Remote Sensing Data |
| title_short | An Overview of Coastline Extraction from Remote Sensing Data |
| title_sort | overview of coastline extraction from remote sensing data |
| topic | coastline extraction remote sensing deep learning remote sensing knowledge map |
| url | https://www.mdpi.com/2072-4292/15/19/4865 |
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