AI自动整合全球数据与模型，精准预测灾害、健康等地理问题。
Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings
- 通过自然语言指令自动获取多源地理数据并融合基础模型嵌入
- 在美疾控21项指标上平均提升R²至76.8%，超人工基准16.8个百分点
- 适合政策制定者、科研人员快速部署高精度地球系统预测
为应对粮食安全、灾害风险、疾病暴发和经济社会脆弱性等全球挑战，亟需高保真地理空间建模。但构建预测性行星模型受限于碎片化数据生态，需手动获取、多模态数据整理与融合，以及迭代模型选择。我们提出行星预测引擎（PPE），一个可直接响应自然语言查询的自主AI系统，实现从头到尾的端到端工作流。PPE实时合成多模态数据集，从开放网络与地球观测平台（Data Commons、Google Earth Engine）检索时空相关协变量，并与地理空间基础模型嵌入（PDFM、AlphaEarth）融合。同时，它在任务定制的模型架构族中搜索，并配备自动过拟合防护机制。在多样任务、区域与科学领域中，PPE持续优于最先进或人工调优的专家基线。在美国空间回归任务中，其在21个疾控中心健康指标上平均R²达76.8%（对比基准60.0%），联邦应急管理局全国风险指数达64.9%（对比60.0%），社会脆弱性指数达66.2%（对比58.6%）。在数据稀缺场景下的空间降尺度任务中，PPE通过引入局部代理变量，使尼日利亚粮食安全指标的准确率翻倍（R² 66.1% vs. 31.5%）。在2026年刚果民主共和国邦迪布吉约埃博拉疫情的流行病学实时预报中，PPE实现召回率@10为83.3%（在五次周度预测中识别出18个新入侵卫生区中的15个），较公开最先进模型提升10.3个百分点（约73%）。通过结合自主多模态行星数据发现与目标模型优化，PPE降低了行星尺度分析的技术门槛，实现快速、定制化、专家级部署。
原文摘要 · Abstract (English)
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). Simultaneously, it searches over task-tailored model architecture families with automated overfitting guards. Across diverse tasks, geographies, and scientific domains, PPE consistently outperforms state-of-the-art or manually tuned expert baselines. For US spatial regression, PPE improves mean $R^2$ across 21 CDC health indicators (76.8% vs. 60.0%), FEMA national risk indices (64.9% vs. 60.0%), and the Social Vulnerability Index (66.2% vs. 58.6%). For spatial downscaling in data-scarce settings, PPE integrates localized proxies to double baseline accuracy in Nigerian food security indicators ($R^2$ of 66.1% vs. 31.5%). For epidemiological nowcasting of the 2026 DRC Bundibugyo Ebola outbreak, PPE achieves a Recall@10 of 83.3% (identifying 15 of 18 newly invaded health zones across five weekly forecasts), a +10.3 percentage-point improvement over the public state-of-the-art modeling (~73%). By combining autonomous multimodal planetary data discovery with targeted model optimization, PPE lowers the technical barrier to planetary-scale analytics, enabling rapid, customized, expert-level deployment.
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