Cecil is partnering with Space Intelligence to make nature data accessible

Cecil is partnering with Space Intelligence to provide audit-grade land cover data through Cecil’s data platform.

Space Intelligence is on a mission to stop deforestation and enable forest conservation and restoration around the globe. As a leading provider of high accuracy nature mapping data, Space Intelligence supports a variety of use cases across a range of clients, including Apple, Verra, and Equinor.

Cecil’s platform includes an API and analytical database, providing seamless access to a variety of nature datasets. With Cecil, engineering and data teams can securely connect their data operations and leverage a modern SQL database in their workflow to analyse time-series and spatial data at scale.

This collaboration will enable data and engineering teams to access Space Intelligence datasets through the Cecil platform.

To learn more about Space Intelligence datasets, explore Cecil’s documentation (https://docs.cecil.earth/) or book a call to speak with a team member about access.

Overview: Space Intelligence

Space Intelligence produces land cover and land cover change mapping data across over 20 tropical countries, with more countries regularly added. Space Intelligence’s process is rooted in decades of research and experience, with senior staff having published more than 100 research papers on remote sensing, ecology, and machine learning.

Annual land cover maps with 10 m or 25 m spatial resolution are generated on a country to country basis by combining optical and SAR satellite data with machine learning models and ecologist knowledge to predict 9 land cover classes, such as dense forest, tree crops, agriculture, and plantation. Model predictions undergo rigorous benchmarking, achieving more than 90% overall accuracy through independent point sampling and manual checks by expert ecologists.

Accessing Space Intelligence datasets on Cecil

Cecil makes nature data consistent, joinable, and ready for analysis with its platform built for data science and engineering teams. Cecil empowers users to perform large scale analyses without the need for building infrastructure or upfront pricing quotas.

Cecil’s Python SDK includes an API and analytical database, providing seamless access to datasets like Space Intelligence land cover and land cover change datasets. With Cecil data teams can securely connect their data operations and leverage a modern SQL database in their workflow to analyse time-series and spatial data at scale. There is no minimum spend or subscription to get started with Cecil, making high-quality datasets accessible to organizations of all sizes with usage-based pricing.

Land cover data in nature data workflows

The high overall accuracy and country-by-country specificity of Space Intelligence land cover data makes it suitable for sophisticated use cases that require audit-grade LULC data. Below, we outline three examples of such use cases.

Due diligence and screening

High quality land cover and land cover change dataset are critical for making good decisions about where to site, and whether to invest in, in a new or existing carbon project. This applies to any type of project that involves a change in land cover, including reforestation (ARR), avoided deforestation (REDD+), and improved forest management projects.

High quality datasets can responsibly answer important assessments like whether an area planned for reforestation has been non-forest for at least ten years (a requirement of most carbon standards), there is a genuine risk to a forest area from deforestation, or an area is suitable for tree harvesting. 

Further, independent assessments of if an existing project has the same distribution of landcover types and has achieved the same outcomes as claimed in their documents is critical to investment decisions. It is only possible to come to firm conclusions on such assessments if the maps being used are known to be best-in-class, as with Space Intelligence’s data; if a lower quality source of LULC data is used, then it is not possible to know if differences are due to genuine poor performance, or simply errors in the maps.

Forest carbon project origination 

Space Intelligence’s high-quality data supports project developers with choosing where exactly to draw their project boundaries, deciding which areas are eligible for their activities under their chosen methodology, and completing their Project Documents. The high quality of the landcover maps reduces the uncertainty in all such assessments, making the process of moving a project through audit and to registration easier, and maximising the number of credits generated through a reduction in uncertainty discounting. 

Space Intelligence’s strong reputation for quality maps, and the fact that these maps are produced independently of the project developer, increases confidence in the carbon credits generated and therefore increases the price at which these credits are sold.

Monitoring and Impact Assessment of forest carbon projects

With annually updated data on land cover and land cover changes, both developers and investors can monitor project impact. High-accuracy data enables reliable assessment of additionality, leakage, and credit integrity for monitoring reports and impact assessments. This helps those generating or using carbon credits to confidently demonstrate their project's real-world impact to stakeholders and the public.