Building footprint and volume extraction
Reconstruct built-up geometry for urban planning, urban modification analysis and building inventory enrichment.
Technologies
AI for Feature Detection
XSensee uses proprietary AI models to detect and classify features that are difficult or impossible to extract consistently from raw 10 metre imagery, especially when paired with super-resolved multispectral inputs.
Pixels become inventories, alerts and map-ready layers
Detection models transform imagery into business-ready outputs such as footprints, counts, classifications and priority maps for operational teams.
Why it matters
Why feature detection matters
Most users do not buy images, they buy decisions. Feature detection shortens the path from acquisition to action by turning visual evidence into structured outputs that analysts, GIS teams and business stakeholders can use immediately.
Capabilities
- Converts imagery into machine-readable entities such as buildings, parcels, ponds, mines or waste deposits.
- Supports large-area screening with consistent logic across repeated territories and time windows.
- Produces outputs that fit downstream GIS, reporting and API-driven operational workflows.
Best-fit applications
Where teams use this technology
These are some of the highest-value workflows that become practical when this capability is deployed inside XSensee.
Broad-area site detection
Detect fish farms, open-pit mines or plastic waste deposits across wide territories to support screening and prioritization.
Asset risk and obstruction analysis
Identify relevant surface features near critical assets and corridors so intervention teams can focus on the highest-priority locations.
Talk to the team
Want to see the technology mapped to your use case?
Tell us the territory, assets and decision problem you need to solve. We will map the right sensing and analytics workflow for your team.