Coastal erosion and shoreline change are accelerating worldwide, driven by sea-level rise, intensifying storm activity, and growing human pressure on coastal zones. Monitoring these changes accurately and at scale is essential for coastal management, infrastructure planning, and climate adaptation, yet traditional field-based survey methods remain labour-intensive, spatially limited, and difficult to sustain over the long timeframes needed to detect meaningful trends.
Satellite remote sensing offers a path to consistent, large-scale coastal observation, and recent advances in machine learning are transforming how this data can be processed and interpreted. Automated shoreline extraction, change detection, and predictive modelling are now feasible at resolutions and frequencies that were previously impractical. However, translating these techniques into reliable, scalable monitoring tools remains challenging. Key barriers include inconsistent shoreline-extraction methods across studies, a lack of standardised validation against ground-truth data, and the difficulty of accounting for environmental variability such as tides, wave climate, and vegetation cover, which can all confound automated detection.
This Research Topic aims to advance the science and practice of coastal erosion monitoring by bringing together studies that combine satellite remote sensing with machine learning approaches, with a particular focus on methods that can be validated, standardised, and scaled across different coastal settings. We welcome contributions that address technical innovation as well as those that tackle the practical challenges of applying these tools consistently in real-world monitoring programmes.
We invite original research articles, reviews, mini-reviews, and methodological studies covering, but not limited to:
- Shoreline extraction and change detection using satellite imagery
- Standardisation and validation approaches for remote sensing-derived shoreline data
- Accounting for environmental variability (tides, waves, seasonality, vegetation) in erosion monitoring
- Scalable and transferable monitoring frameworks across coastal regions
- Machine learning and deep learning approaches for coastal change classification and prediction
- Integration of multi-source satellite data (optical, SAR, multispectral) for shoreline analysis
- Case studies demonstrating operational or near-real-time coastal monitoring systems
Submissions without a clear satellite remote sensing or machine learning component, or that do not address coastal erosion or shoreline change specifically, fall outside the scope of this collection.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Community Case Study
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
