---
title: "行星预测引擎：基于智能数据选择与基础模型嵌入的自主地理空间预测"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.26088v1"
permalink: "https://feed.astralor.com/items/itm_b7a197757bbfd98ac8243597"
published: "2026-08-26T17:50:52.000Z"
score: 78
channel: "ai"
tags: ["地理空间基础模型","数据共享平台","自主AI","行星预测引擎","谷歌地球引擎"]
---

# 行星预测引擎：基于智能数据选择与基础模型嵌入的自主地理空间预测

## AI 点评

> 这篇论文真正值得关注的是它把'预测地球'从手工流水线变成了一个自然语言就能驱动的自主系统，尤其对没有遥感或ML背景的政策制定者和公共卫生团队意义重大。如果你关注AI for Good或地理空间智能，PPE的工程路径很值得借鉴。

## 速览

行星预测引擎（PPE）是一个自主AI系统，能直接从自然语言查询出发，自动完成多模态数据检索、融合与模型优化。它整合开放网络和地球观测平台数据，并结合地理空间基础模型嵌入，在不同任务上显著超越专家基线。在美国21项CDC健康指标中，平均R²从60.0%提升至76.8%；在数据稀缺的尼日利亚粮食安全指标中，R²从31.5%翻倍至66.1%；对2026年刚果（金）埃博拉疫情，Recall@10达83.3%，优于公开最新模型。

## 关键点

- PPE实现从自然语言查询到预测结果的完全自动化，无需人工干预。
- 集成Data Commons、Google Earth Engine等数据源，结合PDFM、AlphaEarth等地理空间基础模型嵌入。
- 在美国健康、灾害风险和社会脆弱性等回归任务上，平均R²提升6.4到16.8个百分点。
- 在刚果（金）埃博拉疫情预测中，Recall@10达83.3%，比公开最先进模型高10.3个百分点。

## 全文

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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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_b7a197757bbfd98ac8243597