Technology
Geospatial data meets practical AI.
GeoVeyra's technology foundation combines Earth observation imagery, scalable spatial processing, and machine learning to help teams surface useful geographic insights.
Architecture
From pixels to a reviewed finding.
Five layers, each one keeping the metadata the next one needs.
- 01
Sources
- Optical imagery
- Radar imagery
- Elevation and land cover
- Boundaries and assets
- 02
Catalog and storage
- STAC metadata
- Cloud Optimized GeoTIFF
- GeoParquet vectors
- Object storage
- 03
Processing
- Quality screening
- Alignment and masking
- AI inference
- Asynchronous jobs
- 04
Review
- Evidence alongside findings
- Confirm or correct
- Audit history
- 05
Delivery
- Maps and dashboards
- Reports
- Alerts
- API and exports
Data ingestion
The right observation for the question.
Optical satellite imagery from public Earth observation programs, such as Copernicus Sentinel-2 and USGS and NASA Landsat, covers much of the planet at regular intervals. Synthetic aperture radar, such as Sentinel-1, adds observations through cloud and at night where relevant.
Openly licensed elevation, land-cover, meteorological, and boundary datasets add context, and licensed commercial imagery can add finer detail where agreements exist. Every source is used subject to its current data terms.
Cloud-native formats
Built on open geospatial standards.
STAC catalogs make imagery searchable by place, time, and properties. Cloud Optimized GeoTIFFs stream only the tiles an analysis needs. GeoParquet and GeoJSON carry vector results efficiently and portably.
AI analysis
AI as an analytical assistant, not an oracle.
Models surface where to look. People decide what it means. Observed imagery changes are kept separate from inferred causes.
- Computer vision for satellite image interpretation
- Semantic segmentation and land-cover classification
- Multitemporal image comparison and change detection
- Anomaly detection for selected geographic indicators
- Geospatial feature extraction and raster and vector analytics
- Human-in-the-loop review for uncertain observations
- Model evaluation on geographically diverse imagery and seasons
Infrastructure
Designed to scale with the size of the question.
- Cloud-native
- Imagery is read by the tile and catalogued by metadata, not copied around in bulk.
- Asynchronous
- Large analyses run as background jobs you can track, instead of blocking a screen.
- Containerized
- Processing and inference scale with the size of the area and the question.
- Secured
- Authentication, authorization, encryption, tenant separation, and audit logging.
Bring your technical questions to a walkthrough.
Tell us what you want to monitor or understand. Let's explore how geospatial intelligence could fit your workflow.