New research: Enabling coastal analytics at planetary scale

Coastal science is evolving rapidly, driven by open satellite data, cloud platforms, and models that nowadays can achieve comparable resolutions for local and broad-scale studies. Altogether, this allows us to better understand and manage coastal risks at scales that were unimaginable just a decade ago. But while the resolution of the analyses increases, so does the challenge of managing, accessing, and analyzing these massive datasets efficiently.

At Deltares, we explored how cloud technology enables coastal analyses at scale. Our work introduces the Global Coastal Transect System (GCTS), a foundational dataset of over 11 million transects at 100-meter resolution that supports scalable coastal analytics and is excellent for regional comparisons. 

Our work aims to show that if the coastal community wants to address urgent challenges at scale—without sacrificing accuracy or resolution—it probably has to embrace cloud technology.

Key highlights from the study

• Position: Addressing global coastal challenges requires scalable data repositories, tools and models
• Foundational dataset: GCTS provides a global dataset of over 11 million coastal transects at 100-meter resolution, ideal for coastal analytics and regional comparisons.
• Scalability: Cloud-native methods can map coastal waterlines at 50 kilometers per second—up to 700 times faster than traditional approaches—enabling high-resolution analyses that scale from local applications to broad-scale studies.
• Critical findings: One-third of the world’s first kilometer of coastline lies below 5 meters—areas often vulnerable to accelerating sea-level rise—highlighting the need for climate adaptation planning. 
• Next steps: As a community, we must work together to establish the foundations for scalable, transparent, and reusable coastal research.

Cloud technology to elevate coastal science 

Cloud technology already plays a critical role in modern science—particularly in fields that rely on large datasets such as satellite data catalogs. It is becoming equally crucial in coastal science as global flood maps and other coastal datasets are increasingly produced at resolutions previously reserved for local studies.

An essential component of this shift is using cloud-optimized data formats, such as Cloud-Optimised GeoTIFF (COG), (Geo)Parquet, and Zarr. These formats allow selective access to specific parts of a dataset, eliminating the need to download or load the entire file into memory. Like streaming a movie scene rather than downloading the entire film, cloud-optimised formats enable efficient access to large geospatial datasets.

The performance gains are substantial. In one of our benchmark tests, a common GIS task—retrieving data for a specific region of interest (e.g., the Basque Country)—took nearly 20 minutes using traditional formats like Shapefiles or GeoPackages. The same task was completed in under seven seconds with a cloud-native setup and could run on a much smaller computer. 

Cloud systems are also designed to scale. Instead of relying on a single computer, data is stored across vast networks of machines, enabling parallel access. Rather than pulling all the data through one narrow straw, cloud technology lets us use as many straws as needed—dramatically increasing throughput.

An equally important feature is data-proximate compute, which brings code to the data rather than vice versa. This minimises delays caused by data transfer and enables rapid processing directly where the data is stored.

Together, these two principles—cloud-optimised formats and data-proximate compute—make it possible to scale local methods to continental and even global domains, such as waterline mapping. In our study, this setup enabled us to map waterlines at 50 kilometers per second. Cloud-native workflows rely on metadata—structured summaries that describe the contents of a dataset—to streamline and optimise processing. Rather than loading entire files into memory, cloud systems use metadata to determine exactly which parts of the data are needed and when. It’s like scanning a table of contents before reading a book—knowing where to look before turning each page.

None of this is possible without standardised, well-structured data. While the broader cloud-native community has made significant progress in developing formats and protocols for scalable analysis, coastal science still needs to define standards that fit its specific needs. This is not just a technical task—it’s a shared responsibility. As a community, we must work together to establish the foundations for scalable, transparent, and reusable coastal research.

Global Coastal Transect System (GCTS): a foundational dataset for coastal analytics

This paper introduces the Global Coastal Transect System (GCTS), a novel foundational dataset comprising over 11 million coastal transects at 100-m resolution. Cross-shore coastal transects are essential to coastal monitoring, offering a consistent reference line to measure coastal change, while providing a robust foundation to map coastal characteristics and derive coastal statistics thereof. The transect system is computed in the UTM projection to avoid zonal bias that was present in earlier systems. The system also comes with administrative fields (continent, country, region), making it ideal for robust statistical regional comparisons.

Built with scalability in mind, the study uses GCTS as the foundation to analyze low-lying coastal regions across the world. The analysis combines GCTS, a vector dataset, with DeltaDTM, that is high-resolution elevation data stored in rasters. Typically integrating both data types at scale is challenging, but cloud-native changes the game. By using cloud-optimised data formats and organizing GCTS so that it is ideal for analytics (geospatial partitions),  the team achieved processing speeds up to 700 times faster than traditional methods. Our case study, about global coastal elevation shows that 33% of the world’s first kilometer of coastline lies below 5 meters—highlighting the vulnerability of coastal zones to sea-level rise and the pressing need for large-scale climate adaptation planning.

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Access the Global Coastal Transect System (GCTS) data

While the data is available in a Zenodo repository for download, we highly recommend direct access via the cloud. The data is described in a STAC Collection part of the CoCliCo STAC catalog. For usage instructions, please see this tutorial or the documentation in the public TU Delft GitHub repository CoastPy—which also contains the codes used to conduct this analysis and much more! 

Read the full paper here:

Floris Reinier Calkoen, Arjen Pieter Luijendijk, Kilian Vos, Etiënne Kras, Fedor Baart, Enabling coastal analytics at planetary scale, Environmental Modelling & Software, Volume 183, 2025, 106257, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2024.106257 

Abstract:

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 capitalise 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-optimised 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.

Keywords:

Coastal analytics; Cloud technology; Coastal change; Coastal monitoring; Satellite-derived shorelines; Low elevation coastal zone; Data management

This blog was written by Floris Calkoen (Deltares) and Jacinta Hamley (Vizzuality)