GeoAI.

Technologies

• Semantic segmentation and land cover Pixel-level classes such as water, built-up, crops and burn scars from imagery.
• Change detection Finding what changed between dates, from new structures to landslides and flood extent.
• Object detection and counting Locating assets, vehicles, vessels and damage in imagery and video.
• Training data and labels Curated, versioned, licensed training sets with provenance and class definitions.
• Model metadata and model cards Recording inputs, bands, resolution, training data and limits with every model.
• Accuracy assessment Sampling design and area-adjusted accuracy so map errors are reported honestly.
• Edge inference on imagery Running models beside cameras and drones so alerts arrive without moving raw video.
• Geospatial foundation models Large pretrained models for imagery and maps fine-tuned with few labels.
• Super-resolution and multi-sensor fusion Learning to combine sensors and resolutions into sharper or gap-free products, with uncertainty flagged.
• Geospatial agents and natural-language queries Assistants that compose spatial queries and processes from plain-language questions, with every step logged.