GIS and AI-powered coastal defense: How tech is fighting shoreline erosion

The integration of AI, satellite data, GIS, UAVs, and GTD marks a paradigm shift from reactive to proactive coastal management. This hybrid monitoring framework not only allows for continuous shoreline surveillance but also enables accurate erosion prediction, supporting early-warning systems and spatial planning decisions.

The global threat of coastal erosion is intensifying, with nearly 80% of the world’s shorelines affected by the dual forces of natural events and human activities. A new study published in Applied Sciences titled “A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring” comprehensively maps how integrated technologies are being deployed to track, analyze, and mitigate this growing crisis.

This systematic review led by Demetris Christofi and colleagues from the Cyprus University of Technology and the University of the Aegean synthesizes 58 peer-reviewed studies between 2021 and 2024. It evaluates how open-access satellite missions, particularly Sentinel-2 and Landsat 8/9, are being integrated with artificial intelligence (AI), Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), and ground truth data (GTD) to create a robust framework for shoreline monitoring. The findings serve as a technological blueprint for governments, coastal engineers, and environmental planners in the face of accelerating climate-driven sea-level rise.

What challenges does shoreline monitoring face, and how are satellite missions addressing them?

The study reveals that traditional shoreline monitoring methods, such as field surveys and aerial photography, suffer from infrequent updates, high costs, and limited spatial coverage. These constraints make them inadequate in the context of fast-changing environments, such as rapidly urbanizing deltas or cyclone-prone tropical coasts.

Enter the Sentinel and Landsat satellite missions. Landsat, operated jointly by NASA and the USGS, offers a unique temporal depth, capturing global multispectral imagery since 1972. Sentinel-2, developed by the European Space Agency (ESA), enhances this archive with higher spatial resolution (10 meters) and a revisit frequency of five days. Together, these satellites provide an optimal balance of historical perspective and contemporary resolution. The synergy between Sentinel’s rapid monitoring capability and Landsat’s historical continuity enables both long-term erosion trend detection and near-real-time assessments.

Data fusion strategies like pixel stacking and temporal harmonization are increasingly employed to maximize their combined effectiveness. Notably, these approaches allow researchers to overcome cloud coverage issues and track short-term changes with greater fidelity. The datasets have proven especially effective in identifying erosion hot spots, seasonal shoreline variability, and even sediment transport dynamics across diverse coastal morphologies.

How do AI, UAVs, and GIS tools complement satellite data in monitoring coastal erosion?

The review emphasizes that satellite data, while indispensable, gain greater utility when integrated with supporting technologies. Geographic Information Systems (GIS), for instance, provide the spatial framework to visualize, quantify, and predict shoreline movement. One key tool cited is the Digital Shoreline Analysis System (DSAS), developed by the USGS, which offers metrics like End Point Rate (EPR), Linear Regression Rate (LRR), and Net Shoreline Movement (NSM) to analyze decadal trends and forecast future coastline shifts.

Artificial intelligence dramatically amplifies the efficiency and accuracy of shoreline segmentation. Models like WaterNet, a deep learning ensemble that integrates U-Net variants, FC-DenseNet, and GAN-based Pix2Pix, achieve shoreline extraction accuracies surpassing 99% from Landsat imagery. These models not only automate classification but also generalize well across different coastal settings, overcoming limitations of manual digitization and human bias.

UAVs fill another critical gap, i.e. resolution. Equipped with RTK-GNSS and LiDAR, drones provide ultra-high-resolution terrain models that satellites cannot match, especially in localized studies. They excel in monitoring features such as beach cusps, dune retreat, or storm-induced erosion. Their on-demand deployment makes them ideal for rapid response scenarios where satellite revisit intervals fall short. Moreover, UAV data are often used to validate satellite-derived shorelines and correct model errors through Ground Truth Data (GTD) collection.

What is the future of adaptive coastal management based on this research?

The integration of AI, satellite data, GIS, UAVs, and GTD marks a paradigm shift from reactive to proactive coastal management. This hybrid monitoring framework not only allows for continuous shoreline surveillance but also enables accurate erosion prediction, supporting early-warning systems and spatial planning decisions.

Several reviewed studies have already demonstrated successful implementations. For example, predictive models like the Kalman Filter are being used to simulate shoreline positions in 2031 and 2041 along vulnerable Indian coasts. UAV-assisted surveys in Ghana reveal the unintended consequences of poorly designed sea defenses, while Landsat-Sentinel fusion analysis in Vietnam provides robust long-term erosion forecasts despite seasonal cloud cover.

Despite these advances, the review also acknowledges current limitations. Atmospheric distortions, cloud cover, and spatial heterogeneity in sediment composition continue to challenge remote sensing accuracy. The authors suggest the development of adaptive AI models that can learn and recalibrate across diverse coastal environments. Integrating high-resolution commercial satellite data and enhancing data fusion algorithms are also recommended next steps.