1.1.  Aims

The initiative explores innovative methods to unify disparate datasets and models into consistent workflows. It highlights practical solutions through demonstrators and prototypes that align with international geospatial standards. These efforts are grounded in real-world scenarios that emphasize interoperability across agencies and domains.

This report (D001) compiles the outcomes, lessons learned, and experiences from the Pilot. The content emphasizes:

1.2.  Objectives

The FMSDI Pilot focuses on two primary objectives:

2.1.  The Land/Sea Interface: Definition and Importance

The inter-tidal and nearshore region, also referred to as the “white ribbon”, is the area of the shoreline that alternates between being exposed to air during low tide and submerged underwater during high tide. This dynamic region serves as the boundary between terrestrial and marine ecosystems and undergoes constant changes due to tidal cycles.

Figure 1 — Natural shoreline gradation illustrating the land-sea interface. Source: NSW Department of Primary Industries – Fish Habitat Network.

The intertidal zone holds significant ecological importance as a habitat for a diverse array of species, including plants, animals, and microorganisms that are uniquely adapted to survive in both aquatic and terrestrial conditions. It supports intricate food webs, provides breeding grounds for marine species, and functions as a natural filter by trapping sediments and processing nutrients, thereby enhancing water quality.

Economically, the intertidal zone contributes through resources such as fisheries, aquaculture, and tourism. It also plays a crucial role in coastal protection by absorbing wave energy and mitigating the impacts of storm surges. Environmentally, this zone offers valuable insights into changes such as sea-level rise, erosion, and the effects of climate change. It serves as a natural laboratory for studying sediment dynamics, coastal morphology, and the interaction of biological and physical processes.

The intertidal zone also holds cultural and recreational value, being a space for traditional practices, heritage activities, and leisure pursuits like fishing and tide pooling. Furthermore, it is pivotal for sustainable coastal zone management and urban planning, influencing policies related to conservation, development, and disaster mitigation.

2.2.  Tides Geography

Tides, as well known, are the periodic rise and fall of sea levels caused primarily by the gravitational forces exerted by the Moon and the Sun on Earth. The interaction of these forces with Earth’s rotation generates tidal bulges in the oceans. As Earth rotates, these bulges shift, creating the high and low tides experienced along coastlines. The Moon’s gravitational pull has the most significant influence, with the Sun’s gravity acting as a secondary force, modifying the intensity of tides depending on their alignment with Earth.

Tidal patterns vary globally due to factors such as the Earth’s tilt, ocean basin shapes, and coastal configurations. There are three main tidal patterns: diurnal, semidiurnal, and mixed. Diurnal tides consist of one high and one low tide each day, common in locations like the Gulf of Mexico. Semidiurnal tides, observed in areas like the Atlantic coast, feature two high and two low tides of approximately equal height within a day. Mixed tides, prevalent in the Pacific, display two high and two low tides of unequal height daily.

Spring and neap tides further illustrate tidal variability. Spring tides occur during full and new moons when the Sun, Moon, and Earth align, amplifying tidal forces and resulting in higher high tides and lower low tides. Conversely, neap tides occur during the first and third quarters of the lunar cycle when the Sun and Moon are at right angles to Earth, diminishing tidal effects and producing lower high tides and higher low tides.

Local geography plays a significant role in shaping tidal behavior. Coastal and underwater topography can amplify tidal ranges in narrow inlets, bays, or estuaries, leading to phenomena like tidal bores. In contrast, open ocean regions may experience minimal tidal changes due to the lack of such constraining features.

In addition to the vertical rise and fall of water, tides generate tidal currents, which are horizontal water movements associated with changing tides. Flood currents carry water inland as the tide rises, while ebb currents move seawards as the tide falls. These currents influence sediment transport, nutrient cycling, and coastal erosion, making them essential to both natural processes and human activities.

Tidal patterns are also subject to long-term variations due to factors such as Earth’s axial tilt, orbital eccentricity, and the reshaping of ocean basins. Over decades or centuries, these changes can alter tidal ranges and frequencies, impacting coastal ecosystems and infrastructure.

The effects of climate change are expected to amplify tidal impacts. Rising sea levels will increase the baseline water level, intensifying high tides and making low-lying areas more vulnerable to flooding. These shifts highlight the importance of understanding and predicting tidal behavior to mitigate risks and protect coastal environments.

2.3.  Challenges in Harmonizing Land and Marine Data

Harmonizing land and marine data presents numerous challenges stemming from the distinct approaches, priorities, and methodologies of the respective communities. One significant challenge lies in differing scaling practices. Land data often focuses on localized, high-resolution features, while marine data typically emphasizes broader, regional scales to accommodate vast oceanic expanses. This disparity complicates efforts to create consistent models that accurately represent both environments.

Temporal aggregation poses another obstacle. Terrestrial data may prioritize long-term trends and static features, whereas marine data often requires high-frequency updates to account for dynamic conditions such as tides, currents, and seasonal variations. Aligning these temporal frameworks is essential but requires sophisticated integration techniques to avoid information loss or misrepresentation.

Data density further complicates harmonization efforts. Land data is frequently collected at a high density in urban or economically significant areas, while marine data often suffers from uneven coverage, especially in remote or deep-sea regions. This uneven distribution results in gaps that hinder the creation of comprehensive, interoperable datasets.

Figure 2 — Land/Sea Data Integration Challenges

This diagram summarizes key technical challenges in the integration of intertidal data—an area that inherently lies at the boundary between land and sea. It highlights five interrelated categories of complexity:

These challenges are inherently interdependent. Solving one often exacerbates or reveals others. For instance, transforming data to a common vertical datum may expose inconsistencies in resolution or sampling frequency. The cyclic nature of these challenges reinforces the need for integrated strategies and holistic data models.

In addition semantic differences in terminology, data quality standards, and methodologies across agencies exacerbate the challenge.

3.1.  Semantic Uplift and Mapping for Cross-Domain Transformation

Harmonizing data between the land and marine domains involves addressing several intricate challenges to achieve seamless interoperability. One of the key aspects is the need for semantic uplift and the application of mapping rules for cross-domain data transformation. The complexity is heightened by the need to address differences in scaling, temporal aggregation, and data density between land and marine datasets. These challenges require innovative approaches and tools to ensure that data integration occurs with minimal loss of information and maximum fidelity to the original datasets.

Semantic uplift is an essential process in cross-domain data transformation, where data from one domain is enriched to align with the conceptual models and semantics of another domain. This ensures that data originally designed for land or marine applications can be accurately interpreted and utilized in the other domain with minimal information loss.

Semantic uplift involves adding context, metadata, and attributes to the source data to enhance its expressiveness and compatibility. For example, a marine dataset describing bathymetric features might require additional annotations to align with terrestrial elevation models, such as vertical datum transformations or feature type classifications.

Mapping rules form the foundation of the transformation process, defining how features, attributes, and relationships in the source data correspond to those in the target domain. These rules ensure consistent and predictable data conversion and typically include:

3.2.  Minimizing Information Loss in Dynamic Data Integration

Integrating dynamic data across land and marine domains requires employing strategies that minimize information loss. This is essential when dealing with datasets that differ in resolution, temporal scales, and semantic contexts. Several approaches support preserving data fidelity and usability:

4.1.  Best Practice 1: Unified Geospatial Reference

Accurate integration of coastal data requires all observations—whether from land or sea—to reference a common geodetic framework. Disconnected vertical and horizontal datums between topographic and hydrographic datasets remain one of the most persistent technical barriers to land-sea integration. This is especially critical in the context of coastal inundation modelling, where mismatches in reference systems can lead to significant errors in flood risk assessments.

A Geodetic Reference Frame provides the essential baseline for referencing features consistently across land, sea, and the coastal interface. Best practices emphasize the use of a modern vertical control network, incorporating GNSS Continuously Operating Reference Stations (CORS) and geoid-based height systems. These allow elevation data—such as those from LiDAR or bathymetric sonar—to be referenced to a gravity-based vertical datum, minimizing distortions from local tidal or ellipsoidal frames.

To harmonize datasets:

Land-sea elevation integration is not just about technical accuracy—it’s foundational for delivering trustworthy, interoperable datasets that can support planning, risk reduction, and long-term resilience. Without a unified reference frame, even the most advanced models or visualizations risk misrepresenting reality.

4.2.  Best Practice 2: FAIR Data Principles

Ensure datasets are Findable, Accessible, Interoperable, and Reusable (FAIR) by providing comprehensive metadata and aligning with established international standards. When working across land and sea domains, metadata plays a critical role in determining whether data is fit for purpose—particularly when sources differ in coordinate systems, data quality, or lineage.

Each data product should:

To enable machine-readable access and discovery:

Where applicable, formats like Bathymetric Attributed Grid (BAG) should be used to encapsulate both elevation and vertical uncertainty in a single product.

Adhering to FAIR principles reduces duplication, supports AI-enabled workflows, and builds long-term value into your coastal datasets through transparency and reusability.

4.3.  Best Practice 3: Mind the Gap

The “white ribbon”—the intertidal zone between land and sea—is often poorly surveyed due to overlapping jurisdictional responsibilities and challenging environmental conditions. This results in persistent data voids that hinder coastal modelling, flood risk assessments, and infrastructure planning.

Where land and marine datasets do not overlap:

Note that interpolation is a last resort. While cost-effective, it risks misrepresenting seabed morphology, especially in dynamic coastal systems.

When filling gaps:

Closing the white ribbon gap ensures spatial continuity, improves model fidelity, and supports decision-making across land-sea interfaces.

4.4.  Best Practice 4: Coordinated Governance

Governance is as critical as technology when it comes to integrating intertidal and coastal data. The coastal zone sits at the boundary of multiple jurisdictions—land agencies, hydrographic offices, environmental authorities—each with different mandates, data standards, and legal frameworks. Without coordinated governance, efforts become fragmented, data remains siloed, and key information gaps persist.

The report emphasizes the importance of:

A recommended framework is the IGIF-Hydro Umbrella Governance Model, which:

Adopting such a model enables agencies to:

Ultimately, governance must evolve in parallel with technical capability to deliver on the promise of a seamless, resilient, and usable land-sea geospatial infrastructure.

4.5.  Best Practice 5: Scalable Resolution Management

Land and marine data are frequently collected at different spatial resolutions, driven by environmental constraints, technology limitations, or differing mandates. Attempting to force uniform resolution across these datasets can result in loss of accuracy, artificial smoothing, or misleading artefacts.

Instead of resampling or generalizing, adopt scalable and flexible data structures that preserve detail where needed:

These structures support:

When upsampling or downsampling is unavoidable:

The objective is to retain local accuracy without sacrificing broader context. By embracing resolution-aware methods, practitioners can create continuous coastal elevation models that respect the complexity of source data while remaining usable across diverse applications.

4.6.  Supporting Content

For expanded technical context, implementation guidance, and case-based insights, refer to the full Draft Guide and Best Practices Engineering Report (~39 pages, 30–40 min read). It covers:

You may access the full report here: Read Online (HTML) | Download PDF

5.1.  Compusult (D100): Coastal Erosion Monitoring and Prediction

5.1.1.  Purpose

The purpose of Compusult’s demonstration for this project is to showcase the integration of terrestrial and marine data sets into a unified operational picture, and to explore challenges encountered when integrating land and sea data from heterogeneous sources.

 

Figure 3 — Examples of Land-Sea Data Integration

5.1.4.  Platform

 

Figure 4 — Web Enterprise Suite (WES)

Compusult’s Commercial-Off-The-Shelf (COTS) Web Enterprise Suite (WES) serves as the demonstration platform.

WES is a modular system built upon Open Geospatial Consortium (OGC) and ISO standards, supporting integration with a wide variety of commercial and open-source geospatial tools and services.

Web Enterprise Suite product information is available at https://www.webenterprisesuite.com.

The WES demonstration portal for FMSDI 5 can be accessed at https://wes-ogc.compusult.com.

5.1.4.1.  Data Integration Portfolios

Two thematic portfolios were developed to demonstrate land and sea data integration:

5.1.4.1.1.  The Solent, UK

The portfolio integrates datasets from:

• Ordnance Survey Maps API

• UKHO S-102 Bathymetric Surface

• UKHO Seabed Mapping Service

• UKHO Wrecks and Obstructions (Shapefiles)

 

Figure 5 — Data Integration Portfolio - The Solent, UK

5.1.4.1.2.  Chesapeake Bay, USA

The portfolio integrates datasets from:

• US Geological Survey (USGS) Imagery Topo Map

• NOAA National Geodetic Survey (NGS) Continually Updated Shoreline Product (CUSP)

• NOAA Historical Composite Shoreline

 

Figure 6 — Data Integration Portfolio - Chesapeake Bay, USA

5.1.4.2.  Vessel Navigation Visualization

The vessel navigation service implements an OGC API Process and OGC API Map to visualize navigable waters in intertidal zones of The Solent and Chesapeake Bay.

5.1.4.2.1.  OGC API – Processes

An OGC API Process calculates navigable and non-navigable waters.

User inputs include:

• Area of interest

• Vessel draught (in meters)

• Simulated tidal surge (in meters)

• Date and time (for historical tide data)

Datasets used:

• Digital Elevation Model derived from National Coastal Asset Register

• UKHO Admiralty Seabed Mapping Service bathymetry

• OceanWise tidal data (real-time and historical)

 

Figure 7 — Process Wizard

5.1.4.2.2.  OGC API – Maps

The vessel navigation process outputs an OGC API Map:

• Navigable waters shown in blue

• Non-navigable waters shown in red

• Intertidal zones represented with a light gray ribbon

 

Figure 8 — Vessel Navigation Scenario Map

5.1.4.2.3.  Cesium 3D Map Client

The WES 3D client visualizes the vessel navigation scenario in a three-dimensional environment using CesiumJS.

 

Figure 9 — WES 3D Map Viewer

5.1.6.  Gaps and Lessons Learned

5.1.6.1.  Data Gaps

Available elevation datasets extended only to the low water mark, requiring bathymetric data supplementation. Observed non-uniformity in bathymetric coverage necessitated interpolation to ensure complete visualizations.

 

Figure 10 — Elevation and Bathymetry Data Gaps

5.1.6.2.  Model Differences

Discrepancies were observed between different shoreline data models (e.g., NOAA CUSP vs. Historical Composite Shoreline), attributed to shoreline evolution and differing data capture methodologies.

 

Figure 11 — Differences Between Shoreline Models

5.1.6.3.  Vertical Datum Consistency

Inconsistencies were encountered when tidal data lacked vertical datum specifications. Early iterations assumed Ordnance Datum Newlyn (ODM), causing errors. After correction to Southampton Chart Datum (-2.74m), water depths displayed correctly.

 

Figure 12 — Vessel Navigation Scenario with Incorrect Vertical Datum

5.1.6.4.  Infrastructure Considerations

The vessel navigation process currently does not account for man-made flood control structures such as berms or sea walls, occasionally resulting in navigability mapping beyond such barriers.

 

Figure 13 — Flood Control Infrastructure Considerations

5.1.7.  Recommendations and Next Steps

Building on the outcomes of the pilot, the next steps and proposed improvements are as follows:

• Expand portfolios with additional land and sea datasets.

• Supplement bathymetry to address data gaps.

• Generate additional navigability maps simulating different tidal or storm surge conditions.

• Enhance the vessel navigation process to account for infrastructure barriers.

• Integrate vector datasets into the 3D client view.

• Provide easy access for participants via direct portal links.

 

Figure 14 — 3D Client View - Portsmouth Harbour

5.2.  Pangaea (D100): Trusted Data Interoperability for Port Operations

5.2.6.  Platform

The TerraNexus FMSDI5 demonstrator is a CesiumJS application viewer embedded into a Django web application. t enables users to conduct OGC API DGGS queries over indexed terrestrial and marine data collections.

Users can define Areas of Interest (AOIs) via bounding boxes or DGGS Zone IDs. The platform then:

• Queries all DGGS-enabled collections for intersecting zones

• Automatically issues OGC API DGGS Zone Data Requests using link templates

• Renders responses as 4D visualizations (e.g., point clouds, 3D buildings) within the Cesium viewer

5.2.6.1.  Platform Landing Page URL

The TerraNexus FMSDI5 Demonstration Platform is accessible at:

https://terranexus.pangaeainnovations.com/fmsdi25

 

Figure 15 — Terranexus The Solent DGGS

 

Figure 16 — TerraNexus Federated Marine Spatial Data Infrastructure Demonstrator — Shaded DGGS Zones

 

Figure 17 — TerraNexus Federated Marine Spatial Data Infrastructure Demonstrator — Wireframe DGGS Zones

The screenshots illustrate marine and terrestrial datasets queried and visualized through DGGS-indexed OGC APIs.

• Marine data: UKHO S-100 bathymetry (1989_HI366_Nab_Tower_to_Old_Castle_Point_Blk2, CSV)

• Terrestrial data: Ordnance Survey bld-fts-buildingpart-2 (OGC API Features)

A DGGS Zone query (bounding box around Cowes, Isle of Wight) retrieves intersecting zones. These zones trigger linked API calls to fetch data, which are rendered in the viewer. The UKHO data appears as a point cloud colored by depth; infrastructure data appears as building footprints or 3D forms.

NOTE:  Key Insight: This approach executes complex, multi-format, multi-CRS spatial queries across jurisdictions using a simple DGGS index lookup, with no need for prior harmonization.

The two screenshots differ only in visual style:

• Shaded Zones: Translucent polygon fills

• Wireframe Zones: Transparent, with edge outlines showing tessellation structure

5.3.  TCarta (D100): Space-Based Remote Sensing for Intertidal Awareness

5.3.4.  Technical Objectives

5.3.4.1.  Hurst-Spit, The Solent, UK

The primary technical goal of this effort was to produce multiple shoreline vectors using space-based sensors, with the collection of these data synchronized with tide-gauges and water-level models in order to produce multiple coastline vectors which can dynamically represent the shoreline at a given point in time, based on the current water height.

To do so, several technical sub-objectives were pursued:

• Use Planet Labs multispectral imagery to derive Normalized Difference Water Index (NDWI) products, exposing the land-sea interface at the time of collection.

• Use Capella SAR data to extract the shoreline at the time of collection using a proprietary TCarta SAR Shoreline toolbox workflow.

• Attribute each shoreline vector with the known water level, based on live tide-gauge data and water-level models, via an application programming interface (API).

• Use a web-mapping application to:

  • Enable both visualization of tide-zone vectors and live, temporally-relevant water level information.

  • Compare and contrast other coastline/bathymetric/terrestrial datasets.

  • Facilitate discussion of how space-based sensors can be used to improve coastal monitoring.

 

Figure 18 — Shoreline Variability – Hurst Spit, UK

 

Figure 19 — Hurst Spit Tide Ranges

5.3.4.2.  Space-Based Bathymetry and Intertidal Mapping

A secondary technical objective was to generate bathymetric and intertidal datasets using publicly available and commercial satellite imagery.

Sub-objectives:

• Generate sub-tidal and intertidal coverage using Sentinel-2 and Planet Labs imagery.

• Integrate in situ bathymetric datasets (UKHO, Ordnance Survey, NOAA) to validate and improve vertical accuracy.

• Deliver outputs via an OGC Web Coverage Service (WCS) for consumption by partner demonstrators and visualization platforms.

5.3.5.  Platform

TCarta deployed a custom ArcGIS Enterprise portal as the demonstration environment. The main application uses the time-slider “instant app” template, enabling real-time comparison of shoreline vectors based on current water levels.

Shoreline vector updates and tidal information are served dynamically using the ArcGIS Python API.

 

Figure 20 — Hurst Spit Tide Ranges Dynamic View (the dynamic view is shown at the html version of the report)

5.3.5.1.  Platform Landing Page URL

The dynamic shoreline viewer is accessible at:

https://arcgis.tcarta-gis.com/portal/apps/instant/slider/index.html?appid=baa6b39ef7034ecc957fcfb4f8ac9bde