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I would like to share my solution to promote this interesting competition further.
I adopted the ChangeStar2 [1] architecture with the object-based post-process from ChangeOS [2] for our entries.
Our single model can achieve 73.34%, ranking top positions on this leadboard for a long time.
The core implementations used in this competition were curated from https://github.com/Z-Zheng/pytorch-change-models

Ref:
[1] Zheng, Z., Zhong, Y., Ma, A., & Zhang, L. (2024). Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection. International Journal of Computer Vision, 1-21.
[2] Zheng, Z., Zhong, Y., Wang, J., Ma, A., & Zhang, L. (2021). Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters. Remote Sensing of Environment, 265, 112636.

Hope guys have a good game :)