内容来源：https://research.google/blog/planetary-prediction-engine-automating-global-models-via-earth-ai/

内容总结：

谷歌研究院近日推出“行星预测引擎”（Planetary Prediction Engine，简称PPE），这是一项实验性人工智能研究能力，隶属于谷歌地球AI（Google Earth AI）计划，旨在通过自动化完成地理空间建模全流程，将全球数据转化为可操作的洞察，以应对公共卫生、粮食安全、环境风险和社会经济等领域的重大挑战。

传统地理空间建模长期受制于数据碎片化、人工处理耗时等瓶颈，专业团队往往需要数周时间进行数据整理和特征工程。现有自动化机器学习工具虽能处理常规流程，但缺乏地理空间工作流的专业能力。PPE系统则通过自然语言查询即可自主执行从数据发现、清理到模型训练和评估的完整流程，生成综合报告，将原本数周的工作缩短至几分钟内完成。

该系统将预测流程分解为三个模块化阶段，均由大语言模型（LLM）协调，各阶段独立运行，数据通过不透明句柄传递，避免上下文窗口限制。在多项基准测试中，PPE均展现显著优势：在美国21项CDC健康指标预测中，平均R²达到76.8%，优于专家人工流程的60.0%；在FEMA国家风险指标和社会脆弱性指数预测中，同样优于基线水平。

针对数据稀缺地区，PPE通过整合局部市场冲击、食品价格异常和微气候指标，将尼日利亚粮食安全预测从省级细化到地方区政府级，R²从31.5%提升至66.1%，准确率提升一倍。在2026年刚果（金）本迪布焦埃博拉疫情实时预测中，PPE成功提前识别18个新入侵卫生区中的15个，召回率达83.3%，较已发表的贝叶斯模型基线提升10.3个百分点。

研究还发现，将统计协变量与潜在基础模型嵌入（如人口动态嵌入和AlphaEarth嵌入）相结合能产生协同效应，多模态融合优于单一数据源。谷歌团队表示，未来计划扩展更多地理空间数据源和基础模型嵌入。PPE的推出降低了行星尺度分析的技术门槛，使研究人员、人道主义组织和政策制定者无需专业工程团队即可快速构建模型，在时间紧迫的决策场景中实现快速部署。

中文翻译：

2026年8月27日

拉玛·帕苏马蒂，高级软件工程师，与斯拉维亚·谢蒂，杰出工程师，谷歌研究院

作为谷歌地球AI计划的一部分，我们推出行星预测引擎（PPE），这是一项实验性研究能力，能够自主执行完整的地理空间建模工作流程——从数据发现到模型训练——在公共卫生、粮食安全、环境风险和社会经济等多个预测任务中实现性能提升。

应对人类最紧迫的全球挑战——从预测区域粮食安全和环境灾害风险，到追踪实时疾病暴发和绘制社会经济脆弱性地图——需要高精度的地理空间建模。然而，构建这些模型受到碎片化数据生态系统的阻碍，需要专业团队花费数周时间进行人工数据整理、特征工程和专门的空间验证。虽然现有的AutoML和基于LLM的智能体能够有效自动化标准机器学习流水线，但它们依赖预先整理好的表格数据，缺乏自主处理地理空间工作流所需的专门能力。因此，行星尺度的分析仍然是一个重大瓶颈，在人道主义危机中需要快速响应时，这一限制尤为严重。

今天我们推出行星预测引擎（PPE），这是我们更广泛的谷歌地球AI计划中最新的一项实验性研究能力，它将行星信息转化为可操作的洞察。此前，我们展示了地球AI在智能整合多样化地理空间资产方面的推理能力。作为一个自主AI系统，PPE直接从自然语言查询出发，执行完整的地理空间预测工作流程，从行星尺度数据发现和清理，到模型训练和评估。给定一个地理空间预测查询，PPE将自主检索相关数据、进行特征工程、训练和评估预测模型，并生成全面报告。我们证明，通过在多样化基准上实现性能提升且无需人工干预，PPE有效将构建复杂行星预测模型所需的时间从包含人工数据工程的数周缩短到产生自主洞察的短短几分钟。

PPE将预测工作流程分解为三个模块化阶段，每个阶段由一个LLM编排：

至关重要的是，每个阶段在定义明确的输入和输出上独立运行，以防止数据瓶颈。数据工件通过不透明句柄在阶段之间传递，而不是序列化到LLM提示中，从而避免上下文窗口限制。

我们在机器学习范式、地理区域和科学领域的多维矩阵上评估了PPE。

在21项CDC健康指标中，PPE的智能数据选择和多模态融合实现了76.8%的平均R²，而人工专家流水线为60.0%。我们在预测FEMA国家风险指标（平均R²为64.9%，基线为60.0%）和社会脆弱性指数（平均R²为66.2%，基线为58.6%）方面也观察到类似的提升。

在数据稀缺地区，粗粒度的区域报告往往掩盖了局部脆弱性。通过自主整合局部市场冲击、食品价格异常和微气候指标，PPE在将粮食安全从省级（ADM1）降尺度到地方政府区域（ADM2）级别时，将基线精度翻倍（R²为66.1%，而基线为31.5%），为人道主义组织提供了可操作的高精度脆弱性地图。

在2026年刚果民主共和国本迪布焦埃博拉病毒暴发期间，对新疾病传播热点的实时预测中，PPE实现了83.3%的Recall@10，在连续五周的前瞻性预测中正确识别了18个新侵入卫生区中的15个。这比已发表的先进贝叶斯建模基线（约73%）绝对提升了10.3个百分点，其驱动力是将流行病学信号与PDFM嵌入以及PPE选择的地理空间协变量相融合。

所有实验中的一个关键发现是，将结构化统计协变量与潜在基础模型嵌入相结合的协同价值。单一模态都无法捕捉全貌：统计协变量提供明确的、可解释的信号，而人口动态嵌入和AlphaEarth基础嵌入则编码了从大规模预训练数据集中学到的复杂非线性模式。我们的消融研究一致表明，多模态融合加上智能数据选择优于基线方法，证实这些表示是互补的而非冗余的。在未来的工作中，我们希望扩展这些能力，纳入更多地理空间数据源和基础模型嵌入，如遥感基础多模态嵌入。

PPE证明，自主AI系统可以在广泛的地理空间预测任务中达到或超越现有性能：从估算美国慢性病患病率，到提高尼日利亚粮食不安全的分辨率，再到对刚果民主共和国活跃病毒暴发进行临近预报。通过结合智能数据发现、多模态基础模型融合和自动化模型优化，PPE降低了行星尺度分析的技术门槛，在时间敏感的决策关键时期实现快速部署。

我们相信，这项实验性研究代表着向地理空间预测民主化迈出的重要一步。通过将焦点从人工数据工程转移到高层假设导向，行星预测引擎帮助研究人员、人道主义组织和政策制定者无需专业工程团队即可构建模型。虽然PPE是一个早期研究项目，我们很高兴探索更多用例，看看这种方法如何帮助组织更好地预测和解决复杂的全球挑战。

我们感谢并认可论文所有合著者的贡献。我们感谢联合国世界粮食计划署（WFP）和脆弱性分析与制图（VAM）团队提供的数据和研究支持。我们还感谢刚果国家生物医学研究所（INRB）在刚果民主共和国埃博拉临近预报方面的合作，以及Data Commons、谷歌地球引擎、人口动态基础模型和AlphaEarth团队为PPE提供的基础数据和模型基础设施。

英文来源：

August 27, 2026
Rama Pasumarthi, Staff Software Engineer, and Shravya Shetty, Distinguished Engineer, Google Research
As part of Google Earth AI, we introduce the planetary prediction engine (PPE), an experimental research capability that autonomously executes the full geospatial modeling workflow — from data discovery to model training — achieving improvements across diverse prediction tasks in public health, food security, environmental risk, and socioeconomics.
Addressing humanity's most pressing global challenges — from forecasting regional food security and environmental disaster risks to tracking real-time disease outbreaks and mapping socio-economic vulnerability — requires high-fidelity geospatial modeling. However, building these models is hindered by a fragmented data ecosystem that requires specialized teams to spend weeks on manual data curation, feature engineering, and specialized spatial validation. While existing AutoML and LLM-based agents effectively automate standard machine learning pipelines, they rely on pre-curated tabular data and lack the specialized capabilities needed to autonomously handle geospatial workflows. Consequently, planetary-scale analytics remains a significant bottleneck, a limitation that is especially severe when rapid response is critical during humanitarian crises.
Today we introduce the planetary prediction engine (PPE), the latest experimental research capability within our broader Google Earth AI initiative that turns planetary information into actionable insights. Previously, we demonstrated Earth AI’s reasoning capabilities in intelligently bringing together diverse geospatial assets. As an autonomous AI system, PPE executes the full geospatial prediction workflow, from planetary-scale data discovery and cleanup to training and evaluation, directly from natural-language queries. Given a geospatial predictive query, PPE will autonomously retrieve relevant data, engineer features, train and evaluate predictive models, and generate a comprehensive report. We demonstrate that by delivering improvements over diverse benchmarks without manual intervention, PPE effectively reduces the time needed to build complex planetary prediction models from weeks that include manual data engineering to mere minutes that result in autonomous insight.
The PPE decomposes the predictive workflow into three modular stages, each orchestrated by an LLM:
Critically, each stage operates independently on well-defined inputs and outputs to prevent data bottlenecks. Data artifacts are passed between stages via opaque handles rather than serialized into LLM prompts, avoiding context-window limitations.
We evaluated the PPE across a multidimensional matrix of machine learning paradigms, geographies, and scientific domains.
Across 21 CDC health indicators, the PPE’s intelligent data selection and multimodal fusion achieve a mean R² of 76.8% vs 60.0%, compared to a manual expert pipeline. We observe similar gains for predicting FEMA national risk indicators (mean R² of 64.9% vs. 60.0% baseline) and Social Vulnerability Index (mean R² of 66.2% vs. 58.6% baseline).
In data-scarce regions, coarse regional reporting often obscures local vulnerability. By autonomously integrating localized market shocks, food price anomalies, and microclimate indicators, PPE doubles baseline accuracy when downscaling food security from the provincial state (ADM1) level to the local government area (ADM2) level (R² 66.1% vs. 31.5%), providing humanitarian organizations with actionable, high-fidelity vulnerability maps.
For real-time prediction of new disease transmission hotspots during the 2026 Bundibugyo ebolavirus outbreak in the Democratic Republic of the Congo, the PPE achieves a Recall@10 of 83.3%, correctly identifying 15 of 18 newly invaded health zones across five sequential weekly forecasts. This represents a +10.3 percentage point absolute improvement over the published state-of-the-art Bayesian modeling baseline (~73%), driven by fusing epidemiological signals with PDFM embeddings and PPE-selected geospatial covariates.
A key finding across all experiments is the synergistic value of combining structured statistical covariates with latent foundation model embeddings. Neither modality alone captures the full picture: statistical covariates provide explicit, interpretable signals, while Population Dynamics Embeddings and AlphaEarth Foundations embeddings encode complex non-linear patterns learned from large pre-training datasets. Our ablation studies consistently show that multimodal fusion along with intelligent data selection outperforms baseline approaches, confirming that these representations are complementary rather than redundant. In future work, we want to expand these capabilities to include more geospatial data sources, and foundation model embeddings such as Remote Sensing Foundations multimodal embeddings.
The PPE demonstrates that autonomous AI systems can match or improve performance across a wide range of geospatial prediction tasks: from estimating chronic disease prevalence in the United States, to improving resolution of food insecurity in Nigeria and nowcasting active viral outbreaks in the Democratic Republic of the Congo. By combining intelligent data discovery, multimodal foundation model fusion, and automated model optimization, the PPE lowers the technical barrier to planetary-scale analytics, enabling rapid deployment when time-sensitive decisions are critical.
We believe this experimental research represents a meaningful step toward democratizing geospatial prediction. By shifting the focus from manual data engineering to high-level hypothesis direction, the planetary prediction engine helps researchers, humanitarian organizations, and policymakers build models without needing specialized engineering teams. While PPE is an early-stage research project, we’re excited to explore more use cases and see how this approach can help organizations better anticipate and solve complex global challenges.
We thank and acknowledge the contributions from all of the co-authors of the paper. We are grateful to the UN World Food Programme (WFP) and Vulnerability Analysis and Mapping (VAM) team for the data and research support. We also thank the Institut National de Recherche Biomédicale (INRB) for their collaboration on the DRC Ebola nowcasting, and the teams behind Data Commons, Google Earth Engine, Population Dynamics Foundation Models, and AlphaEarth for providing the foundational data and model infrastructure that power the PPE.