A Framework for Accurate Estimation of Geospatial Models from Sparse Field Measurements Using Image Processing and Machine Learning

The motivation for our work is driven by the cost of field measurements and by the limitations of currently available physics-based modeling techniques. The goal is to improve our understanding of the underlying physical phenomena and increase the accuracy of geospatial models. Our approach is to interpolate sparse field measurements, apply existing physics-based models, incorporate spatial constraints using image processing techniques, explore utilizing auxiliary raster measurements using machine learning, and perform optimization of all algorithmic parameters in supervised, as well as, in unsupervised manner. Our work led to a prototype solution called Spatial Pattern To Learn (SP2Learn: http://isda.ncsa.uiuc.edu/Sp2Learn/). 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. This approach has been tested using a groundwater recharge and discharge model in Wisconsin which is a well understood system.