Accuracy

• Open-source tools and datasets are incredibly valuable resources and can be a good starting point, depending on budget and project scale. However, they often contain data and time gaps, and their accuracy breaks down when applied at local scales.
• Ocean Ledger fills these gaps with spatially accurate, frequently updated data — particularly useful for projects tied to financial instruments (e.g. biodiversity credits), which demand up-to-date and verifiable baselines.

Usability

• Open-source data can typically be downloaded, but it often lacks flexibility for deeper manipulation.
• Ocean Ledger delivers precise, forecastable data tailored to user needs — and enables integration of field data for comprehensive, context-specific analysis.

Monitoring

• National open datasets are usually updated every 5–10 years, with limited projections and low transparency.
• Ocean Ledger updates every 6 days, provides high-resolution forecasts (every 50–100m), and includes uncertainty maps to support rapid decision-making for changing coastal conditions.

Transparency

• Open data may reference accuracy metrics but rarely discloses full model uncertainties.
• Ocean Ledger provides spatial and temporal uncertainty metrics, F1 scores, and performance data for every model and output.

We distinguish between Primary Satellite-Derived Data Products and Advanced Insights. The latter are enhanced outputs that build upon or integrate the core insights from the Primary Products to deliver deeper, more actionable insights.

Currently, our available data products include:

• Primary Satellite-Derived Data Products
• Advanced Insights (enhanced outputs that build on core products for deeper analysis)

‍
Please get in touch to request our latest Tech Report for more information on each data product.

This is the main interface between Ocean Ledger and its clients. Built in-house, the dashboard offers:

• Seamless access (API, raw data downloads) to spatial data products for erosion, bathymetry, natural capital
• Heatmap analysis (with customisable thresholds) to identify erosion, storm surge and ecosystem degradation
• Standardised & Custom Solutions for Scenario Analysis, Climate Modelling & Restoration Uplift
• Trigger thresholds for early warning and risk mitigation
• Accurate baselines and continuous monitoring
• Transparency on data sources, accuracy, and uncertainty metrics

Yes! Please fill out our online form, and we’ll get back to you with options to demonstrate our solution for your project.

Most flood-risk and cat risk providers leverage the same digital elevation models (DEM’s). Ocean Ledger provides a next-generation DEM that fuses bathymetry, shoreline and natural defenses, which are key variables in identifying where there is more or less storm surge risk along a coastline.

First you can fill out our form to get in touch with the team. We’ll then be in touch to ask for a few key project inputs, including the area of interest, time frame, requested data products or advanced insights and existing or future field data integration. Ocean Ledger will then handle the rest and provide the output via the dashboard, API, insight report or raw data files as requested.

We want to ensure that project results are easily integrable into our clients’ workflow. With our options, we minimise internal friction and optimise internal resources. Outputs can be integrated via our dashboard, as well as through downloadable formats like GeoTIFFs, shapefiles, tabular data, or API queries.

Yes. We offer tiered data packages and licenses to the dashboard depending on your monitoring, resolution and scale requirements. You have access to a dashboard for project management, additional run requests and updates.

Depending on the satellite imagery input, we can update data products and models daily, weekly, monthly, quarterly or annually based on your needs. We can also set up alerts and trigger thresholds to automatically update the baseline and models once an event occurs (e.g cyclone).

Ocean Ledger uses a mix of open-source and private satellite providers. The choice of imagery depends on your project’s needs, resolution requirements, and site location. All the options are discussed during the proposal phase.

Resolution depends on the data product but typically can range from 0.5 to 30 meters, depending on the required time scale, project size and insights required. Historical data is available back to the 1980s at a 30m resolution. High-res sources at 0.5mm are available from 2001 onward.

Depending on the data product and resolution, we can provide annual forecasts over the next 20 years and model these under climate scenarios for long-term climate and habitat modelling.

We use RCP 8.5 because the other scenarios do not significantly diverge from other scenarios until after 2050. Research suggests other scenarios underestimate climate conditions.

Our models account for data quality (e.g., cloud cover, resolution) and site-specific variables (e.g., mudflats, topography). We provide time and location-specific uncertainty maps with every project to help you interpret confidence levels in the results. We apply rigorous pre-processing and correction workflows, including tidal and atmospheric adjustments. Our models also flag anomalies and trigger checks to maintain accuracy and reliability.

We’re committed to transparency and will detail the reference data input, method and performance metrics.

‍

Yes! You can upload field data directly via the dashboard. We work with partners to guide field data acquisition with an in-house sampling method. We’ll work with your team to build capacity on how to efficiently collect, store and format the data so we can easily integrate with our models.

Please get in touch with our team and we’ll be happy to assist with scoping a bespoke model or data product.

Yes. Our geospatial processing is underpinned by 8 years of R&D at the German Aerospace Center. We are currently developing two ecosystem condition methods under the Accounting for Nature® Framework, a global scientific verifier for natural capital accounting. One method is for Mediterranean seagrass and the is for global mangrove ecosystems. Stay tuned!