GrokLST: Towards High-Resolution Benchmark and Toolkit for Land Surface Temperature Downscaling
<|reference_start|>GrokLST: Towards High-Resolution Benchmark and Toolkit for Land Surface Temperature Downscaling: Land Surface Temperature (LST) is a critical parameter for environmental studies, but obtaining high-resolution LST data remains challenging due to the spatio-temporal trade-off in satellite remote sensing. Guided LST downscaling has emerged as a solution, but current methods often neglect spatial non-stationarity and lack a open-source ecosystem for deep learning methods. To address these limitations, we propose the Modality-Conditional Large Selective Kernel (MoCoLSK) Networks, a novel architecture that dynamically fuses multi-modal data through modality-conditioned projections. MoCoLSK re-engineers our previous LSKNet to achieve a confluence of dynamic receptive field adjustment and multi-modal feature integration, leading to enhanced LST prediction accuracy. Furthermore, we establish the GrokLST project, a comprehensive open-source ecosystem featuring the GrokLST dataset, a high-resolution benchmark, and the GrokLST toolkit, an open-source PyTorch-based toolkit encapsulating MoCoLSK alongside 40+ state-of-the-art approaches. Extensive experimental results validate MoCoLSK's effectiveness in capturing complex dependencies and subtle variations within multispectral data, outperforming existing methods in LST downscaling. Our code, dataset, and toolkit are available at https://github.com/GrokCV/GrokLST.<|reference_end|>
@article{dai2024groklst:,
    title={GrokLST: Towards High-Resolution Benchmark and Toolkit for Land Surface
  Temperature Downscaling},
    author={Qun Dai and Chunyang Yuan and Yimian Dai and Yuxuan Li and Xiang Li
  and Kang Ni and Jianhui Xu and Xiangbo Shu and Jian Yang},
    journal={arXiv preprint arXiv:2409.19835},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19835},
    primaryClass={cs.CV eess.IV}
}