Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to visualize, compare, cluster, and perform similarity search over defined regions of interest using Google Earth Engine as a backend. Sims is designed to complement existing modeling tools by focusing on feature exploration rather than model creation. We demonstrate the utility of Sims through a case study analyzing simulated maize yield data in Rwanda, where we evaluate how different combinations of soil, weather, and agronomic features affect the clustering of yield response zones. Sims is open source and available at https://github.com/microsoft/Sims

翻译：获取、处理和可视化地理空间数据需要大量计算资源，尤其是在大型时空域中。这一挑战阻碍了预测特征的快速发现，而这对推进地理空间建模至关重要。为解决此问题，我们开发了相似性搜索工具（Sims），这是一款无代码网络工具，允许用户以Google Earth Engine为后端，在定义的感兴趣区域上进行可视化、比较、聚类和相似性搜索。Sims旨在通过专注于特征探索而非模型创建，来补充现有建模工具。我们通过分析卢旺达模拟玉米产量数据的案例研究，展示了Sims的实用性，其中评估了土壤、天气和农艺特征的不同组合如何影响产量响应区的聚类。Sims是开源工具，可在https://github.com/microsoft/Sims获取。