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 are essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims). This no-code web tool enables users to perform clustering and similarity search over defined regions of interest utilizing Google Earth Engine as its 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<\/p>\n","protected":false},"excerpt":{"rendered":"
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 are essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims). 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