用AI模型扩展地理数据覆盖范围，让地表植被图跨国可用
Scalable Geospatial Data Generation Using AlphaEarth Foundations Model
- 基于AlphaEarth模型生成全球地理特征，辅助扩展已有数据集
- 在美加两国植被分类任务中分别达到81%和73%准确率
- 即使简单模型也能有效泛化，适合缺乏标注数据的区域
高质量标注的地理空间数据对理解地球至关重要，但往往局限于特定地理区域。谷歌深度思维发布的AlphaEarth Foundations（AEF）提供了一个信息密集的全球地理空间表征，可作为多种任务的输入。本文提出并评估了一种利用AEF扩展地理标注数据集覆盖范围的方法。研究表明，即使是随机森林或逻辑回归等基础模型，也能有效完成此任务。以美国LANDFIRE的现有植被类型（EVT）数据集为例，将数据扩展至加拿大，在两个粒度级别（EvtPhys：13类；EvtGp：80类）上进行验证。定性分析显示，模型预测与真实情况一致。在美国和加拿大的验证集上，分类准确率分别达到81%和73%，尽管存在一些局限性。
原文摘要 · Abstract (English)
High-quality labeled geospatial datasets are essential for extracting insights and understanding our planet. Unfortunately, these datasets often do not span the entire globe and are limited to certain geographic regions where data was collected. Google DeepMind's recently released AlphaEarth Foundations (AEF) provides an information-dense global geospatial representation designed to serve as a useful input across a wide gamut of tasks. In this article we propose and evaluate a methodology which leverages AEF to extend geospatial labeled datasets beyond their initial geographic regions. We show that even basic models like random forests or logistic regression can be used to accomplish this task. We investigate a case study of extending LANDFIRE's Existing Vegetation Type (EVT) dataset beyond the USA into Canada at two levels of granularity: EvtPhys (13 classes) and EvtGp (80 classes). Qualitatively, for EvtPhys, model predictions align with ground truth. Trained models achieve 81% and 73% classification accuracy on EvtPhys validation sets in the USA and Canada, despite discussed limitations.
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