Work

Automating shoreline extraction from beach imaging systems for coastal monitoring

Year: 2026

Type: article

Abstract: High-frequency coastal monitoring is essential for understanding shoreline dynamics under climate change and supporting coastal adaptation. Although ground-based remote sensing offers a scalable solution for data acquisition, automated shoreline extraction from beach imaging systems remains challenging due to variable image quality, complex geometries, and environmental conditions. This study presents a robust deep learning framework for instantaneous shoreline extraction from crowd-sourced imagery (i.e., smartphone captures). We benchmarked state-of-the-art segmentation models against a traditional histogram-based deterministic method. Results show that convolutional neural network approaches, particularly DeepLabV3 with 512 ×512 patches, significantly outperform conventional methods, achieving spatial reliability comparable to manual digitisation. Importantly, the model overcomes perspective distortion and interpolation artefacts in rectified imagery across diverse tidal environments, showing strong environmental robustness despite site diversity and data imbalance. The framework has been deployed as an open-source, containerised module within the European Open Science Cloud, enabling transferable and operational image-based coastline monitoring.

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2026.107078

Authors: Josep Oliver-Sansó, Jesús Soriano-González, Javier Varona, Gabriel Moyà-Alcover, Elena Sánchez-García

Institutions: Balearic Islands Coastal Observing and Forecasting System, Universitat de les Illes Balears

Language: English

Topic: Coastal and Marine Dynamics

Subfield: Earth-Surface Processes

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,500