Nobos provides Benchmarking as a Service, delivering high‑quality, ground‑truth geospatial datasets that are ready for machine‑learning pipelines. By running field campaigns in remote areas and supplying data in standards like STAC, GeoParquet, and GeoJSON with full provenance, it enables climate and biodiversity AI teams to train and validate models with reliable inputs, especially in under‑measured regions of the Global South.
Funding
Funding not disclosed
Founders
Product
Problem
The majority of geospatial data used for climate and biodiversity AI models is missing, coarse, or lacks verifiable provenance, especially across the Global South. This data gap hampers model accuracy and limits reliable environmental monitoring and decision‑making.
Solution
Nobos offers Benchmarking as a Service, creating high‑quality, ground‑truth geospatial datasets that are ready for machine learning. The company runs field campaigns in remote, hard‑to‑reach locations, collects precise measurements, and converts them into standardized formats such as STAC, GeoParquet, and GeoJSON. Each dataset includes full provenance and validation, enabling users to assess and improve the accuracy of existing satellite or crowdsourced data. By structuring the data for direct ingestion into AI pipelines, Nobos helps climate and biodiversity teams train and evaluate models on reliable, verifiable inputs, reducing uncertainty in predictions and policy recommendations.
Target Audience
Primary customers are climate and biodiversity AI developers, research institutions, and NGOs that require accurate geospatial training data for models, particularly those operating in under‑measured regions of the Global South.
Features
- End‑to‑end workflow: scoping and red‑team analysis, network activation, protocol design, field execution, and data processing
- Global network of field scientists, explorers, and domain experts to access hard‑to‑reach environments
- Collection protocols focused on accuracy, reproducibility, and machine‑learning compatibility
- Delivery of benchmark datasets in ML‑ready standards (STAC, GeoParquet, GeoJSON) with complete provenance metadata
- Validation services that compare user‑supplied data against newly collected ground truth
- Capability to gather data in areas where satellite observations are unavailable or unreliable