Field-level parametric insurance index inputs

Field-level parametric insurance index inputs: roughly 35 variables computed per individual crop field — crop type, planting and harvesting dates, soil and management practices, irrigation, tillage, cover crops, planting schedules and severe-weather risk exposure — refreshed on a near-daily satellite and weather monitoring cycle. The platform emits per-field yield-impact estimates when a peril threshold is crossed, and the same feature layer is extended into automated farmland valuation, agricultural credit-risk analysis, verified sustainability reporting, prevented-planting determination and nutrient-runoff observation

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Coverage

Universe, instruments and categories.

Industry: ReinsuranceAgricultural ServicesApplication Software
Instruments: equitiescommoditiesderivativesstructured_products
Categories: AlternativeInsurance LossGeospatialSupply ChainClimate Risk
Regions: USEurope
Sample tickers: RNRHRGSIGIPGRADMDECTVASXI.SWMUV3.DE

Dataset card

Type: not stated
Format: Tabular per-field feature vectors and time series keyed to field geometry, derived from optical and radar satellite imagery plus weather data; delivered as an analytics and API layer to insurer and lender systems rather than as raw imagery
Volume: Order of 100M-1B field-date observation records. Basis: near-daily revisit, roughly 200-300 observations per field per year, multiplied by the field count across a multi-country monitored footprint — but constrained by a company at a $1-10M revenue band and 11-50 employees, which caps area under active commercial monitoring well below continental wall-to-wall coverage. The ~35-variable feature vector per field per observation makes each record unusually wide
Users: N/A in consumer terms — a B2B analytics and API vendor whose customers are insurers, lenders, asset managers and agribusinesses. Institutional client base est. low dozens (inferred from 11-50 employees, a $1-10M revenue band, and a partnership model where the client carries underwriting and claims)
History: not stated
Update frequency: not stated
Growth: Active — AI Modeling v2 launched in 2025 for more accurate in-season yield prediction, leadership expanded with a new chief executive and chief technology officer in January 2025 plus a sales-director hire, the company exhibited at 10 industry conferences globally in 2025, and it was named Agricultural Insurance Data Provider of the Year 2026
Launched: ~2014-2016 founding, with corporate profiles disagreeing between 2014 and 2016, spun out of University of Wisconsin-Madison remote-sensing research by Dr. Mutlu Özdogan; the commercial satellite insurance product reached paying insurers est. 2017-2019 after the seed rounds
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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