GeoZoom enhances Sentinel-2 satellite imagery from 10-meter to 1-meter spatial resolution using an AI-powered super-resolution platform. This 10x improvement in detail, while preserving spectral and radiometric fidelity across all 13 bands, enables precise geospatial analysis for applications like urban planning and environmental monitoring.
Funding
$150K 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
Standard satellite imagery, such as Sentinel-2, offers a 10-meter spatial resolution, which is insufficient for detailed geospatial analysis and monitoring of fine-scale features. This limitation hinders applications requiring precise identification of urban infrastructure, agricultural field variations, or small-scale environmental changes.
Solution
GeoZoom provides an AI-powered super-resolution platform that enhances Sentinel-2 imagery from 10-meter to 1-meter spatial resolution. The system processes all 13 spectral bands of Sentinel-2 data, preserving critical spectral and radiometric fidelity through a specialized convolutional neural network architecture. This advanced processing enables a 10x improvement in spatial detail, making previously indistinguishable features clearly discernible. The platform offers an API and Python SDK for seamless integration into existing geospatial workflows, delivering a cost-effective alternative to acquiring commercial high-resolution satellite data.
Target Audience
The primary users are geospatial analysts, urban planners, agricultural technologists, and environmental scientists who require enhanced spatial detail from satellite imagery for precise analysis and monitoring.
Features
- AI-driven super-resolution engine that upscales Sentinel-2 imagery from 10m to 1m spatial resolution.
- Comprehensive processing of all 13 Sentinel-2 spectral bands, maintaining spectral and radiometric integrity.
- Specialized convolutional neural network (CNN) architecture trained on extensive datasets for optimal super-resolution performance.
- Python SDK with support for GDAL and Rasterio for integration into remote sensing analysis pipelines.
- API access for programmatic image enhancement and integration into cloud-based workflows.
- Optimized GPU implementation for efficient processing of large satellite scenes.