Checkpoints of RS3DAda model trained on the SynRS3D dataset Neural Information Processing Systems (Spotlight), 2024 For more details, please refer to our paper and visit our GitHub repository. Overview TL;DR:We are excited to release two high-performing models for height estimation and land cover mapping. These models were trained on the SynRS3D dataset using our novel domain adaptation method, RS3DAda. Encoder: Vision Transformer (ViT-L), pretrained with DINOv2 Decoder: DPT, trained from scratch These models excel in tasks involving large-scale global 3D semantic understanding from high-resolution remote sensing imagery. Feel free to integrate them into your projects for enhanced performance in related applications. Citation If you find SynRS3D useful in your research, please consider citing: @article{song2024synrs3d, title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery}, author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto}, journal={arXiv preprint arXiv:2406.18151}, year={2024} } Contact For any questions or feedback, please reach out via email at song@ms.k.u-tokyo.ac.jp. We hope you enjoy using the pretrained RS3DAda models!