GeoLearn: An exploratory framework for extracting information and knowledge from remote sensing imagery

This paper presents an exploratory framework called GeoLearn for extracting information and knowledge from large size remote sensing imagery. GeoLearn has been prototyped as a novel simulation and exploratory environment for prediction modeling from remote sensing imagery, and large size geospatial raster and vector data. The GeoLearn framework has the functionality to read data sets from local and remote sites; extract features like slope from elevation; mosaic tiles; perform quality assurance of remotely sensed images; integrate images; spatially select pixels by masking with boundaries, geo-points, maps with categorical variables, thresholded maps with continuous variables or painted regions using primitives; extract pixels over a mask, perform data-driven modeling using machine learning techniques, provide interpretation of models in terms of variable relevance and visualize a variety of input, output and intermediate data. We illustrate the application of the framework to exploring vegetation greenness as a function of climate, terrain, water and soil.