VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting
<|reference_start|>VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting: In the context of global climate change and frequent extreme weather events, forecasting future geospatial vegetation states under these conditions is of significant importance. The vegetation change process is influenced by the complex interplay between dynamic meteorological variables and static environmental variables, leading to high levels of uncertainty. Existing deterministic methods are inadequate in addressing this uncertainty and fail to accurately model the impact of these variables on vegetation, resulting in blurry and inaccurate forecasting results. To address these issues, we propose VegeDiff for the geospatial vegetation forecasting task. To our best knowledge, VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes, enabling the generation of clear and accurate future vegetation states. VegeDiff also separately models the global impact of dynamic meteorological variables and the local effects of static environmental variables, thus accurately modeling the impact of these variables. Extensive experiments on geospatial vegetation forecasting tasks demonstrate the effectiveness of VegeDiff. By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods, providing clear and accurate forecasting results of future vegetation states. Interestingly, we demonstrate the potential of VegeDiff in applications of forecasting future vegetation states from multiple aspects and exploring the impact of meteorological variables on vegetation dynamics. The code of this work will be available at https://github.com/walking-shadow/ Official_VegeDiff.<|reference_end|>
@article{zhao2024vegediff:,
    title={VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting},
    author={Sijie Zhao, Hao Chen, Xueliang Zhang, Pengfeng Xiao, Lei Bai and Wanli
  Ouyang},
    journal={arXiv preprint arXiv:2407.12592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12592},
    primaryClass={cs.CV}
}