Joint Instance Segmentation and Geometric Attribute Regression for Roof Structures in Aerial Imagery
We present a method for jointly predicting instance-level roof segment masks together with three continuous geometric attributes -- building height, roof slope, and roof azimuth -- from a single aerial orthophoto. Our approach extends Mask R-CNN with a dedicated attribute regression branch and introduces two key innovations: a conditional azimuth loss that suppresses supervision for flat roof segments where azimuth labels are inherently noisy, and a log-normalized height representation that addresses the heavily skewed distribution of building heights. We train and evaluate on a large-scale dataset of Dutch aerial images paired with automatically derived ground truth from 3DBAG, a nationw...
| Authors | Luuk Versteeg, Rob G. J. Wijnhoven, Martin R. Oswald |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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