Opleno

Advancing satellite AI and geospatial intelligence

Our research focuses on computer vision for satellite imagery, change detection algorithms, predictive modeling, and natural language generation for geospatial data. We're pushing the boundaries of what's possible with automated Earth observation.

Research Areas

Computer Vision for Satellites

Advanced deep learning models trained to understand satellite imagery, identify features, and detect changes across different resolutions and conditions.

Change Detection Algorithms

Novel algorithms that automatically identify meaningful changes in satellite imagery while filtering out noise, weather effects, and seasonal variations.

Predictive Intelligence

Machine learning systems that analyze historical patterns to predict future changes, project completion timelines, and emerging risks.

Geospatial Analysis

Sophisticated spatial analysis techniques that understand relationships between features, detect patterns across regions, and provide contextual intelligence.

Automated Reporting

Natural language generation systems that translate visual data into clear, plain-language reports explaining what changed, why it matters, and what to do next.

Multi-Source Fusion

Combining data from multiple satellite sources, sensors, and temporal periods to create comprehensive understanding of observed areas.

Current Focus

Real-Time Change Detection at Scale

Active Research Project

Developing systems that can process continuous satellite feeds to detect meaningful changes within hours of capture, enabling near real-time monitoring of critical infrastructure and construction projects.

Contextual Intelligence Generation

Active Research Project

Building AI models that understand context around detected changes—not just identifying that something changed, but explaining why it's significant and what actions might be needed.

Predictive Infrastructure Monitoring

Active Research Project

Creating forecasting models that predict infrastructure issues, construction delays, and environmental risks before they materialize, enabling proactive intervention.