Another Earth provides an AI‑driven simulation platform that generates fully annotated, photorealistic multispectral satellite imagery for land, water, and infrastructure. By procedurally creating unlimited variations and consistent temporal sequences, it supplies unbiased synthetic datasets that accelerate geospatial AI model training and enable scenario testing for monitoring, prediction, and decision‑making across industries such as mining and environmental monitoring.
Funding
$1.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Earth observation projects often struggle with limited access to high-quality, annotated satellite imagery, especially for remote regions, rare objects, or specific temporal scenarios. This scarcity leads to biased training data, high labeling costs, and delayed model development, hindering timely insights into environmental and infrastructure changes.
Solution
Another Earth offers an AI-driven simulation platform that generates fully annotated synthetic satellite imagery across land, water, and infrastructure domains. By procedurally modeling the planet’s surface, the engine creates unlimited variations of multispectral data, including rare object instances and consistent temporal sequences for change detection. Users can tailor scenarios to test future conditions, fill data gaps, and reduce bias without the expense of manual labeling. The synthetic datasets integrate directly into geospatial AI pipelines, accelerating model training and enabling actionable insights for monitoring, prediction, and decision‑making at scale.
Target Audience
Primary customers are AI developers, remote‑sensing analysts, and decision‑makers in sectors such as mining, raw material exploration, and environmental monitoring who require large, labeled geospatial datasets for model training and scenario analysis.
Features
- Procedural generation of photorealistic, multispectral satellite images with pixel‑perfect labels
- Unlimited scenario variations for rare objects, specific land cover types, and future condition modeling
- Consistent temporal datasets to support change detection and predictive analytics
- High‑resolution output with adjustable spatial detail to match diverse sensor specifications
- Automated bias mitigation by providing balanced, unbiased synthetic samples across regions
- API and data export tools for seamless integration into existing geospatial AI workflows