AWS用Amazon Nova做航空影像搜索，F1分数最高，想搞地理空间搜索的可以参考他们的实验设计。
AWS博客介绍了基于Amazon Bedrock和OpenSearch Serverless构建的可搜索航空影像系统架构。团队使用OpenStreetMap地面实况数据设计了四项实验，对比了嵌入模型、融合策略、字幕生成和搜索方法。其中Amazon Nova Multimodal Embeddings在基准查询中取得了最高的F1分数。该系统最终演变为Vexcel Intelligence产品，为地理空间语义搜索提供了实用指导。
Embed the world: Multimodal AI for searchable aerial imagery at scale
In this post, we walk through the problem space, our architecture on Amazon Bedrock and Amazon OpenSearch Serverless, the evaluation methodology we built on OpenStreetMap ground truth, four experiments that compared embedding models, fusion strategies, captioning, and search methods, and the practical guidance you can apply when building a similar system. You’ll learn which design choices move the needle for geospatial semantic search, including why Amazon Nova Multimodal Embeddings delivered the highest F1 scores across both benchmark queries in our evaluation. The work described here evolved into Vexcel Intelligence, a searchable imagery product.