SP2Learn presents a framework for accurate estimation of geospatial models from sparse field measurements using image processing and machine learning. The goal is to improve our understanding of the underlying physical phenomena and increase the accuracy of geospatial models. A typical process of building a geospatial model includes interpolation of sparse field measurements, application of existing physics-based models, incorporation of spatial constraints using image processing techniques, exploration of auxiliary raster measurements using machine learning, and optimization of all algorithmic parameters in supervised, as well as, in unsupervised manner. SP2Learn allows users to explore the accuracy improvements when several image de-noising techniques with a decision tree machine learning technique are employed, and multiple remote sensing and terrestrial raster measurements are used. For example, we provide test data to illustrate how to incorporate and mine slope, soil type and proximity to water bodies for predicting groundwater recharge and discharge (R/D) rate models.

University of Illinois at Urbana-Champaign
National Center for Supercomputing Applications (NCSA)