This is like an automatic “spot the difference” system for satellite photos taken at different times. It uses advanced pattern-recognition and graph math so the computer can find and highlight where the Earth’s surface has changed, without anyone first telling it what to look for.
Manually inspecting long sequences of satellite images to see what has changed (e.g., new construction, deforestation, infrastructure damage) is slow, expensive, and inconsistent. This approach automatically detects and localizes changes in large satellite image time series without requiring ground-truth labels, enabling faster monitoring of regions of interest for defense, security, environmental, and infrastructure purposes.
Domain-specific model design for satellite time series, plus potential access to large proprietary image archives and annotated evaluation datasets that improve performance in real-world defense and Earth-observation scenarios.
Open Source (Llama/Mistral)
Time-Series DB
High (Custom Models/Infra)
High computational cost and memory usage for processing long satellite image time series at scale, especially when constructing and operating on large graphs over many images.
Early Adopters
Combines deep learning for feature representation with graph-based methods over time-series satellite imagery to perform unsupervised change detection, reducing dependence on labeled data and enabling more flexible monitoring across large regions and time spans.