Fine-grained building roof instance segmentation based on domain adapted
  pretraining and composite dual-backbone
Guozhang Liu Baochai Peng Ting Liu Pan Zhang Mengke Yuan Chaoran Lu Ningning Cao Sen Zhang Simin Huang Tao Wang
The diversity of building architecture styles of global cities situated on
various landforms, the degraded optical imagery affected by clouds and shadows,
and the significant inter-class imbalance of roof types pose challenges for
designing a robust and accurate building roof instance segmentor. To address
these issues, we propose an effective framework to fulfill semantic
interpretation of individual buildings with high-resolution optical satellite
imagery. Specifically, the leveraged domain adapted pretraining strategy and
composite dual-backbone greatly facilitates the discriminative feature
learning. Moreover, new data augmentation pipeline, stochastic weight averaging
(SWA) training and instance segmentation based model ensemble in testing are
utilized to acquire additional performance boost. Experiment results show that
our approach ranks in the first place of the 2023 IEEE GRSS Data Fusion Contest
(DFC) Track 1 test phase ($mAP_{50}$:50.6\%). Note-worthily, we have also
explored the potential of multimodal data fusion with both optical satellite
imagery and SAR data.