Spatial data,
ready for analysis

Cecil makes spatial datasets consistent, accessible, and ready for analysis.

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datasets found
Searching across 40+ datasets View all datasets →
Data marketplace

40+ datasets. One agreement.

Access 40+ commercial and open datasets under a standardized agreement.

Deforestation & Forest Degradation

Forest cover, forest type, forest gain, forest loss - perform EUDR baselining and risk assessments.

Global Forest Watch (WRI) · Joint Research Centre · University of Maryland

Biodiversity & Sensitive Areas

Threatened species, protected areas, key biodiversity areas, biodiversity intactness - for TNFD, SBTN, and CSRD.

IBAT · IUCN · Natural History Museum · Wildlife Conservation Society

Asset Locations

Mines, physical assets, facilities, administrative boundaries - locate sites or entire supply chains and portfolios.

geoBoundaries · GIST Impact · PlanetSapling

Land Use Change

Natural lands, croplands, forests, non-natural lands - baseline landscapes and detect land conversion.

Impact Observatory · SBTN Natural Lands · USDA · USGS · WRI

Soil Moisture

Surface soil moisture, soil water content - drought monitoring, agricultural risk, and reforestation siting.

Lobelia · Planet

Physical & Climate Risk

Floods, cyclones, water stress, wildfire - historic, current, and forecasted risk to physical assets and portfolios.

Emmi · WRI Aqueduct
View all datasets →
Cecil SDK

From discovery to analysis in minutes

Programmatic access to spatial data — consistent across providers, deployed across billions of hectares and thousands of sites.

  • Python, CLI, and agentic interfaces Acquire, validate, and analyse datasets through a single integration.
  • Built-in scientific context Specifications, usage notes, and metadata exposed directly in the SDK for dataset discovery and selection.
  • Mapping & visualization Works alongside QGIS, ArcGIS, Leaflet, and Kepler.gl in existing geospatial workflows.
cecil_demo.ipynb ×
Python 3.11
[1]
12345 678910
import cecil

client = cecil.Client()
# Subscribe to a dataset
sub = client.create_subscription(
  aoi_id=my_aoi_id,
  dataset_id="87251e55-8685-4a45-bf3a-52bfa3829b44"
)
# Load directly as xarray Dataset
ds = client.load_xarray(sub.id)
[1]
<xarray.Dataset>
Dimensions: (time: 24, y: 4096, x: 4096)
Variables:
* time (time) datetime64[ns] 2023-01 ... 2023-12
* agb (time, y, x) float32 dask.array
Attributes:
dataset_id: 87251e55-8685-4a45-bf3a-52bfa3829b44
crs: EPSG:4326
Why Cecil

Built for how data teams actually work

Rigorous science. Consistent data. Workflows that fit your stack.

01

Curated by scientists

Every dataset is vetted, validated, and documented by scientists. Evaluate fitness for use, understand limitations, and build defensible workflows.

02

Consistent, analysis-ready formats

Datasets arrive with standardised metadata, units, and projections. No custom parsers, no schema reconciliation, no guessing.

03

Works with your stack

Plugs into the tools your team already uses. QGIS, ArcGIS, Tableau, Leaflet, kepler.gl, Python, R.

Developers

Get your API key and start building

Develop secure, resilient, and highly scalable workflows with the Cecil SDK.

Get API key →
Enterprise

Need a guided onboarding?

Talk to us about custom datasets, volume-based pricing, or integration services for your team.

Contact sales →