Sims: An Interactive Tool for Geospatial Matching and Clustering
This tool addresses the problem of rapid feature discovery for geospatial modeling, primarily for researchers and practitioners in fields like agriculture, but it is incremental as it complements existing tools rather than introducing a new method.
The authors tackled the challenge of high computing resource demands for geospatial data processing by developing Sims, a no-code web tool that enables clustering and similarity search over regions of interest using Google Earth Engine, demonstrated in a case study on maize yield data in Rwanda.
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 perform clustering and 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