LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong

This is an initial benchmark for Unsupervised Domain Adaptation.

Getting Started


  • pytorch >= 1.7.0
  • python >=3.6
  • pandas >= 1.1.5

Prepare LoveDA Dataset

ln -s </path/to/LoveDA> ./LoveDA

Evaluate CBST Model on the predict set

1. Download the pre-trained weights

2. Move weight file to log directory

mkdir -vp ./log/
mv ./CBST_2Urban.pth ./log/CBST_2Urban.pth

3. Evaluate on Urban test set

bash ./scripts/

Submit your test results on LoveDA Unsupervised Domain Adaptation Challenge and you will get your Test score.

Train CBST Model

From Rural to Urban

bash ./scripts/

Eval CBST Model on Urban val set

bash ./scripts/


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