A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring
This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The...
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2025-04-01
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| author | Demetris Christofi Christodoulos Mettas Evagoras Evagorou Neophytos Stylianou Marinos Eliades Christos Theocharidis Antonis Chatzipavlis Thomas Hasiotis Diofantos Hadjimitsis |
| author_facet | Demetris Christofi Christodoulos Mettas Evagoras Evagorou Neophytos Stylianou Marinos Eliades Christos Theocharidis Antonis Chatzipavlis Thomas Hasiotis Diofantos Hadjimitsis |
| author_sort | Demetris Christofi |
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| description | This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years. |
| format | Article |
| id | doaj-art-dd9698bf5f68476bbf16ab48824e49a5 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-dd9698bf5f68476bbf16ab48824e49a52025-08-20T02:24:43ZengMDPI AGApplied Sciences2076-34172025-04-01159477110.3390/app15094771A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion MonitoringDemetris Christofi0Christodoulos Mettas1Evagoras Evagorou2Neophytos Stylianou3Marinos Eliades4Christos Theocharidis5Antonis Chatzipavlis6Thomas Hasiotis7Diofantos Hadjimitsis8ERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusDepartment of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, GreeceDepartment of Marine Sciences, University of the Aegean, University Hill, 81100 Mytilene, GreeceERATOSTHENES Centre of Excellence, 3012 Limassol, CyprusThis review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.https://www.mdpi.com/2076-3417/15/9/4771sentinel-2sentinel-1Landsatcoastal erosionshorelineremote sensing |
| spellingShingle | Demetris Christofi Christodoulos Mettas Evagoras Evagorou Neophytos Stylianou Marinos Eliades Christos Theocharidis Antonis Chatzipavlis Thomas Hasiotis Diofantos Hadjimitsis A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring Applied Sciences sentinel-2 sentinel-1 Landsat coastal erosion shoreline remote sensing |
| title | A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring |
| title_full | A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring |
| title_fullStr | A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring |
| title_full_unstemmed | A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring |
| title_short | A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring |
| title_sort | review of open remote sensing data with gis ai and uav support for shoreline detection and coastal erosion monitoring |
| topic | sentinel-2 sentinel-1 Landsat coastal erosion shoreline remote sensing |
| url | https://www.mdpi.com/2076-3417/15/9/4771 |
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