AI / ML
Blueprint Segmentation with Mask R-CNN
The Challenge
Indoor mapping needed a way to automatically identify walls, doors, points of interest, and text directly from raw blueprint images.
What We Built
We annotated blueprint imagery in Roboflow and trained a Mask R-CNN model (via Detectron2) for instance segmentation, then converted blueprint coordinates into real-world coordinates using affine transformation.
Where It Stands
Shipped in one month as the computer-vision backbone powering the Mapify indoor navigation apps.