Coastal erosion and flooding are linked, with erosion potentially exacerbating flood extents and risk, but analysis of the combined hazards is limited. This paper describes the CoasTER geographic database specifically designed for the first time to integrate existing information on erosion and other relevant characteristics for Europe's coastal floodplains. The CoasTER database updates and builds on earlier erosion research and data sources. At the European scale, it combines fundamental erosion-relevant information (sediment type, land use, floodplains, geomorphology, historical shoreline movement trend) on a standard shoreline to highlight the potential magnitude of erosion-flood interactions by defining where mobile sediments and coastal floodplains are co-located. It also identifies where morphodynamic response to sea-level rise is constrained due to structures/infrastructure. Results indicate almost 80 % (25 000 km) of the total shoreline length associated with European coastal floodplains (approx. 31 000 km) are composed of mobile sediments, with coastal wetlands being the most prevalent geomorphological type. While accretion is the dominant historical trend for these shorelines, approximately 27 % are currently classed as eroding at over 0.5 m yr−1 over the last 40 years. The majority of floodplain shorelines are associated with either developed or agricultural areas and constraining human structures that occur along almost 8000 km of shoreline. The CoasTER database demonstrates that episodic and/or long-term erosion and coastal flooding is a Europe-wide issue that deserves the attention of local to European decision-makers in order to define a coherent management strategy. ... Read more

This thesis addresses the increasing threat of coastal erosion under accelerating sea-level rise (SLR) by developing a global framework that integrates Earth-observation satellite data, cloud computing, and artificial intelligence (AI). Coastal erosion, a natural adjustment process of shorelines, can become a hazard when it coincides with human settlements, infrastructure, or ecosystems that cannot migrate landwards. A comprehensive understanding of erosion impacts under SLR is essential to inform climate adaptation policies. The research presents a three-part methodology, progressing from technical development to impact assessment at the scale of individual buildings worldwide.

The first part of the research focuses on creating a scalable, cloud-native framework capable of processing petabyte-scale satellite archives. Leveraging deep learning techniques, this framework classifies the global coastline at a 100-meter resolution, producing a high-resolution coastal typology that distinguishes geomorphological features such as sandy beaches, dunes, cliffs, and gravel or shingle coasts. This typology provides the necessary context to model coastal processes accurately and to constrain future shoreline projections to sediment-dominated coasts. The framework’s scalability demonstrates the feasibility of combining large-scale satellite data and AI for global coastal monitoring.

Using the derived typology, the second part of the study produces probabilistic future shoreline projections through an equilibrium-profile model. These projections estimate changes under multiple SLR scenarios, focusing on sandy sediment plains and dune coasts. By intersecting projected shorelines with global building datasets, the study quantifies exposure and identifies potentially impacted assets. The analysis reveals that 40% of the global coast is fronted by sandy, gravel, or shingle beaches—higher than prior estimates—and that 20% of these dynamic coasts are backed by cliffs, restricting natural landward migration. Approximately a quarter of the global coastline has buildings within the first kilometer of land, with around 76 million buildings globally. The “Empirical Setback Zone” metric developed here quantifies the de facto setback maintained by coastal communities, indicating that 10% of developed coasts have first buildings less than 37 meters from the shoreline.

Finally, the study provides a first-order global impact assessment. Under low- and high-emission scenarios (SSP1-2.6 and SSP5-8.5), between 1.6 and 2.4 million buildings are projected to be affected by 2100. These figures are conservative due to model constraints, which limit application to sandy and dune coasts with sufficient nearshore data. Limitations also arise from uncertainties in coastal typology and equilibrium-profile model assumptions, as well as the exclusion of full vulnerability and adaptive capacity assessments.

Scientifically, the thesis contributes both methodologically and thematically. It demonstrates a scalable AI-based workflow for analyzing large satellite datasets and provides globally consistent coastal data products, including a new transect system, high-resolution typology, exposure metrics, and probabilistic future shorelines. These outputs bridge local-scale studies with global analyses, enabling first-order asset-level impact assessments. The findings inform coastal management, supporting prioritization of high-risk areas, and advance coastal science by promoting open datasets, AI-based classification, and community-driven CoastalAI development. Continued support for open data, software, and cloud infrastructure is essential to sustain progress in intelligent coastal management and adaptation planning. ... Read more

Coastal erosion and flooding are projected to increase during the 21^st century due to sea-level rise (SLR). To prevent adverse impacts of unmanaged coastal development, national organizations can apply a land protection policy, which consists of acquiring coastal land to avoid further development. Yet, these reserved areas remain exposed to flooding and erosion enhanced by SLR. Here, we quantify the exposure of the coastal land heritage portfolio of the French Conservatoire du littoral (Cdl). We find that 30% (~40%) of the Cdl lands owned (projected to be owned) are located below the contemporary highest tide level. Nearly 10% additional surface exposure is projected by 2100 under the high greenhouse gas emissions scenario (SSP5-8.5) and 2150 for the moderate scenario (SSP2-4.5). The increase in exposure is largest along the West Mediterranean coast of France. We also find that Cdl land exposure increases more rapidly for SLR in the range of 0–1 m than for SLR in the range 2–4 m. Thus, near-future uncertainty on SLR has the largest impact on Cdl land exposure evolution and related land acquisition planning. Concerning erosion, we find that nearly 1% of Cdl land could be lost in 2100 if observed historical trends continue. Adding the SLR effect could lead to more than 3% land loss. Our study confirms previous findings that Cdl needs to consider land losses due to SLR in its land acquisition strategy and start acquiring land farther from the coast. ... Read more

Coastal science has entered a new era of data-driven research, facilitated by satellite data and cloud computing. Despite its potential, the coastal community has yet to fully capitalize on these advancements due to a lack of tailored data, tools, and models. This paper demonstrates how cloud technology can advance coastal analytics at scale. We introduce GCTS, a novel foundational dataset comprising over 11 million coastal transects at 100-m resolution. Our experiments highlight the importance of cloud-optimized data formats, geospatial sorting, and metadata-driven data retrieval. By leveraging cloud technology, we achieve up to 700 times faster performance for tasks like coastal waterline mapping. A case study reveals that 33% of the world’s first kilometer of coast is below 5 m, with the entire analysis completed in a few hours. Our findings make a compelling case for the coastal community to start producing data, tools, and models suitable for scalable coastal analytics. ... Read more

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications. ... Read more

Muddy coasts provide ecological habitats, supply food and form a natural coastal defence. Relative sea level rise, changing wave energy and human interventions will increase the pressure on muddy coastal zones. For sustainable coastal management it is key to obtain information on the geomorphology of and historical changes along muddy areas. So far, little is known about the distribution and behaviour of muddy coasts at a global scale. In this study we present a global scale assessment of the occurrence of muddy coasts and rates of coastline change therein. We combine publicly available satellite imagery and coastal geospatial datasets, to train an automated classification method to identify muddy coasts. We find that 14% of the world’s ice-free coastline is muddy, of which 60% is located in the tropics. Furthermore, the majority of the world’s muddy coasts are eroding at rates exceeding 1 m/yr over the last three decades. ... Read more