Granular.ai
Satellite & Geospatial alternative data provider · 1 listed dataset
Datasets from Granular.ai (1)
Granular.ai is a United States geospatial artificial-intelligence company that develops computer-vision systems for satellite and aerial imagery. It works primarily on defence and intelligence, disaster response, and research infrastructure, while related commercial products apply aerial-image analysis to property and roofing workflows.[1]
The business operates as Granular Data, Inc. and identifies Somerville, Massachusetts, as its place of origin. Datarade independently lists Granular.ai as a United States provider of satellite and alternative data, although its automatically generated profile also assigns categories that are not explained by the company's current website.[1][2]
What It Does
Granular.ai trains and deploys models that identify patterns or objects in remote-sensing imagery. Stated government applications include industrial-site monitoring, reducing the area analysts must search, and geospatial catalogue discovery. Humanitarian and disaster applications cover wildfire, earthquake, flood, and spill events through operational maps, historical queries, and alerts.[1]
Products and Research Infrastructure
GeoEngine is described as an end-to-end geospatial machine-learning operations pipeline spanning imagery ingestion, model development, and deployment. Neso aggregates more than 21 imagery sources, and GeoSearch provides natural-language queries over image catalogues and model outputs. The company states that GeoEngine and GeoSearch are internal research infrastructure rather than public products.[1]
HADR AI is the outward-facing disaster-monitoring system, with named tools including FireMap, QuakeMap, FloodedLand, and SpillTrack. Inspect.Properties is a commercial spinout for roof measurement, storm history, and insurance or roofing documentation; roofing.io is presented as a separate contractor-operations business. These products share technical foundations but address different buyers.[1]
Data and Methodology
The company's technical model combines imagery acquired from multiple satellite and aerial sources with computer-vision training and deployment workflows. Granular.ai says Neso connects more than 21 sources and that government work has combined Penguin suborbital imagery with BlackSky monitoring. The available independent profile confirms satellite-data activity but does not verify the supplier count, model performance, or government deliverables.[1][2]
Granular.ai reports that QFabric, a change-detection dataset, was presented at the CVPR EarthVision Workshop in 2021 and that GeoEngine was published in a 2022 CVPR workshop. The specific publication URL tested during research was unavailable, so these publication details remain company-reported in this article.[1]
History and Programs
The company timeline says Granular Data, Inc. was incorporated in 2017, Granular.ai was founded in 2019, and geospatial AI co-development with the Air Force Research Laboratory began that year. It also records participation in Techstars Boston in 2020-2021 and the inaugural National Geospatial-Intelligence Agency Accelerator cohort in 2023.[1]
Those program milestones are described by Granular.ai; an NGA page attempted during research did not return accessible content. Datarade confirms the company's broad market presence but publishes no standard pricing and directs users to contact the provider. Public sources do not establish contract values, a complete customer list, universal model-accuracy figures, or current licensing terms.[1][2]
More datasets from Granular.ai
Granular.ai lists 1 datasets on this encyclopedia. The most representative listings:
More about Satellite & Geospatial
Satellite & Geospatial is one of the signal families covered by The Alternative Data Encyclopedia, with 24 providers and 147 datasets listed in the register. For how the signal is generated, its caveats and the compliance considerations, and the full provider list, see the category article.
Further reading
- What is alternative data?Alternative data in investment research: what the term covers, how it differs from data format, and why provenance, timing, access and validation matter.
- Satellite and geospatial signalsCounting the world from orbit: optical imagery, SAR, aerial and drone data as investment signals, what each can see, revisit-tradeoffs, and how raw pixels become tradeable indicators.
- Free alternative data sourcesThe practical list of free alternative-data sources for investors: public filings, official statistics, satellite and earth observation, on-chain data, web archives and open registries, what each covers, what it costs in engineering effort, and where free data falls short.
- Alternative data for investingHow funds turn alternative data into an investment edge: signal families, evaluation, backtesting, compliance and the build-vs-buy decision, mapped to the datasets and providers indexed on this encyclopedia.