Another Earth is a company that is building the predictive data layer for the physical world, moving beyond retrospective monitoring to forward-looking scenario simulation. Using high-resolution synthetic Earth observation data, the platform helps organizations train better AI models, simulate future environmental risks, and model how landscapes evolve over time.
Manual annotation is expensive, slow, and carries human error rates up to 50%. Our proprietary synthetic data engine, Reverie, automatically generates pre-annotated, pixel-perfect datasets on demand. In benchmark testing, models trained on just 500 real images supplemented with synthetic data matched the 0.73 mIoU accuracy score of models requiring 10,000 real images—reducing ground-truth dependencies by 95%.
The platform powers deep-learning applications across several primary sectors:
Agriculture & Forestry: Species classification (Tree AI), biomass estimation, and early disease tracking such as Cocoa Swollen Shoot Virus Disease (CoMPASS).
Mining & Raw Materials: Landscape change prediction, slope stability forecasting, and automated ESG impact reporting (PreVision).
Land Cover & Urban Analysis: Multi-resolution land cover segmentation across large amounts of classes.
Energy & Infrastructure: Asset monitoring, solar panel counting, and predictive flood/disaster risk modeling.
Datasets are exported in standard geospatial formats—including multi-band GeoTIFFs, DSM elevation maps, JSON filrx —for seamless ingestion into cloud environments and AI frameworks like PyTorch or TensorFlow. Because data is algorithmically generated, it eliminates GDPR, and privacy compliance constraints associated with real-world imagery.
Cloud cover and shadows frequently obscure surface details and delay early hazard detection. Through dedicated models and synthetic cloud generation, we render specific cloud types and densities—containing 2.3x more thin clouds than real archives—to train advanced cloud detection and removal algorithms. This clears occluded pixels and applies super-resolution to rebuild clear, actionable imagery.
Manual satellite imagery annotation is slow, expensive, and subject to human error rates that can reduce model accuracy by up to 50%. Another Earth’s engine pairs AI-based generation with procedural 3D enhancement to construct hyper-realistic physical environments. Because the 3D scenes are rendered procedurally, pixel-perfect segmentation masks, DSM elevation maps, and instance labels are calculated automatically at the exact moment of creation—completely eliminating manual labeling costs and human error while generating rich training data in seconds.
