How We Extract Feature Intelligence
A hybrid pipeline — sensor fusion, AI at scale, and expert interpretation — producing data that is both fast and defensible.
Multi-Source Imagery Fusion & Conditioning
Feature extraction is only as reliable as the imagery beneath it. We fuse very-high-resolution optical, multispectral, radar, and archive imagery into a single conditioned environment — radiometrically calibrated, atmospherically corrected, orthorectified, and co-registered to sub-pixel alignment. This gives every extracted feature a consistent geometric and spectral foundation, so results from different dates, sensors, and seasons remain directly comparable.
Deep Learning Detection & Segmentation
Custom-trained convolutional and transformer-based models perform first-pass extraction at scale. U-Net and Mask R-CNN architectures delineate buildings, roads, water bodies, and vegetation, while object detection networks count and locate discrete assets such as tanks, towers, wellheads, vessels, and solar arrays. Models are trained on regional imagery conditions — desert albedo, dust, haze, and dense urban shadow — where generic models underperform.
Expert Validation & Topological Cleaning
Automation supplies throughput; interpretation supplies the standard. Every automated output is reviewed by trained photo-interpreters against source imagery, with ambiguous features escalated rather than guessed. Geometry is then cleaned for gaps, slivers, overshoots, and duplicates, attributes enriched, and completeness and correctness scored against a manually mapped reference sample before anything is released.
Schema-Matched Delivery & Integration
An extraction that will not load cleanly has not been delivered. We match your attribute schema, domain values, coding standards, and coordinate reference system, then deliver as file geodatabase, GeoPackage, GeoJSON, or a direct load into PostGIS or your enterprise geodatabase. Recurring programmes publish through OGC web services and APIs with configurable change alerting.