While　extracting　buildings　from　high-resolution　remote　sensing　imagery　has　been　widely　conducted　in　automatic　surveying　and　mapping,　challenges　remain　in　building　extraction　over　complex　scenes,　particularly　for　densely　rotated　objects　with　fuzzy　boundaries.　This　study　proposes　a　rotation-aware　building　instance　segmentation　network　(RotSegNet)　that　integrates　a　refined　rotated　detector　to　extract　rotation　equivariant　and　invariant　features.　A　boundary　refinement　module　is　added　to　the　segmentation　network　to　extract　fine-grained　boundary　features.　We　evaluated　our　method　using　the　WHU　building　dataset　and　AFCities　dataset.　Our　RotSegNet　generated　a　minimum　of　2.5%　and　0.8%　mean　Average　Precision　(mAP)　on　the　two　datasets　compared　with　other　state-of-the-art　methods,　which　shows　the　superiority　of　our　method.　Results　also　show　that　the　proposed　method　can　produce　regularized　buildings　with　high　geometric　accuracy.