Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings· 行星预测引擎:通过智能数据选择和基础模型嵌入自动进行地理空间预测
Addressing critical global challenges, from food security and disaster risk to disease outbreaks and socio-economic vulnerability, demands high-fidelity geospatial modeling. However, building predictive planetary models remains bottlenecked by a fragmented data ecosystem, requiring manual data retrieval, multimodal data curation and fusion along with iterative model selection. We present the Planetary Prediction Engine (PPE), an autonomous AI system that executes this end-to-end workflow directly from natural-language queries. PPE synthesizes multimodal datasets on the fly, retrieving spatiotemporally relevant covariates across open-web and Earth observation platforms (Data Commons, Google Earth Engine) and fusing them with geospatial foundation model embeddings (PDFM, AlphaEarth). Simulta
利用智能数据选择和基础模型嵌入实现自然语言查询驱动的自动地理空间预测。
- 核心方法
- 提出行星预测引擎(PPE),自动执行从自然语言查询到模型预测的全流程,包括数据检索、多模态数据融合及地理空间基础模型嵌入。
- 适合谁读
- 研究者 / 工程师 / 产品
- 要解决的问题
- 解决从食品安全到灾害风险等全球性问题,需要高精度地理空间模型,但现有数据生态系统碎片化,模型构建过程繁琐。
- 关键实验
- 未提供
- 主要贡献
- 实现自然语言驱动的自动化地理空间预测,提高模型构建效率和精度。
- 意义与局限
- PPE 可显著提升地理空间建模速度,帮助快速响应全球性挑战,但也可能存在数据来源限制和模型泛化能力问题。