用隐式气候嵌入让全球生态研究免去数据下载和训练负担
Climplicit: Climatic Implicit Embeddings for Global Ecological Tasks
- 通过预训练模型生成任意位置的气候嵌入，无需下载原始气候数据
- 相比直接训练模型，节省3500倍存储空间，计算需求大幅降低
- 在生物群落分类等任务中表现优于现有地理编码方法，适合生态学者使用
气候数据上的深度学习在宏观生态学中有巨大潜力，但因存储、算力和专业技能门槛，限制了非深度学习领域科学家的应用。为此，我们提出Climplicit，一种基于时空地理坐标的预训练隐式气候嵌入模型，可生成地球上任意位置的气候表示。该模型避免了下载原始气候栅格数据和训练特征提取器的需求，使下游任务所需磁盘空间减少3500倍，并显著降低计算开销。我们在生物群落分类、物种分布建模和植物性状回归任务上评估了Climplicit嵌入，结果表明，仅通过单层探测即可达到或超过从头训练模型的表现，整体优于其他地理编码模型。
原文摘要 · Abstract (English)
Deep learning on climatic data holds potential for macroecological applications. However, its adoption remains limited among scientists outside the deep learning community due to storage, compute, and technical expertise barriers. To address this, we introduce Climplicit, a spatio-temporal geolocation encoder pretrained to generate implicit climatic representations anywhere on Earth. By bypassing the need to download raw climatic rasters and train feature extractors, our model uses x3500 less disk space and significantly reduces computational needs for downstream tasks. We evaluate our Climplicit embeddings on biomes classification, species distribution modeling, and plant trait regression. We find that single-layer probing our Climplicit embeddings consistently performs better or on par with training a model from scratch on downstream tasks and overall better than alternative geolocation encoding models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。