LoveDA (Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation)
Introduced by Wang et al. in LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
- 5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan
- Focus on different geographical environments between Urban and Rural
- Advance both semantic segmentation and domain adaptation tasks
- Three considerable challenges:
- Multi-scale objects
- Complex background samples
- Inconsistent class distributions
Two contests are held on the Codalab: LoveDA Semantic Segmentation Challenge, LoveDA Unsupervised Domain Adaptation Challenge
Papers
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