AI & Computer Vision
An organisation does not have to send someone to every location to learn what is happening in the field. We convert satellite and drone imagery into measurable information.
In remote sensing the real challenge is not the model but data preparation: differing resolutions, cloud cover, seasonal change and coordinate alignment. We train our models to perform under these real-world conditions.
Every output we produce is georeferenced. A detected object sits in the correct place not only in the image but on the map as well — the result flows straight into the GIS platform.
What we do in this area
Object detection
Automatic detection of buildings, vehicles, agricultural parcels and infrastructure elements from imagery.
Land cover classification
Production of land use maps through pixel-based classification.
Change analysis
Automatic extraction of the difference between two periods; detection of unauthorised construction and land change.
Segmentation
Pixel-level segmentation for work that requires boundary extraction and area calculation.
Drone workflows
The complete workflow, from flight planning through orthophoto production to inventory extraction.
Where does it deliver value?
Detection, classification and prediction models from satellite and drone imagery.
Let's discuss this-
Unauthorised construction and zoning inspection
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Crop pattern monitoring across agricultural land
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Green space and tree inventory extraction
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Post-disaster damage assessment
Why Türkol?
For computer vision output to be useful it has to meet the map. Because we build the GIS side as well, model output feeds directly into the decision-making process.