The motivation for developing Im2Learn (Image to Learn) comes from academic, government and industrial collaborations that involve development of new computer methods and solutions for understanding complex data sets. Images and other types of data generated by various instruments and sensors form complex and highly heterogeneous data sets, and pose challenges on knowledge extraction. In general, the driver for the Im2Learn suite of tools is to address the gap between complex multi-instrument raw data and knowledge relevant to any specific application. The objective of the Im2Learn suite of tools is to research and develop solutions to real life problems in the application areas of machine vision, precision farming, land use and land cover classification, map analysis, geo-spatial information systems (GIS), synthetic aperture radar (SAR) target and multi-spectral scene modeling, video surveillance, bio-informatics, microscopy and medical image processing, and advanced sensor environments. The main goal of the Im2Learn research and development is to automate information processing of repetitive, laborious and tedious analysis tasks and build user-friendly decision-making systems that operate in automated or semi-automated mode in a variety of applications. The development is based on theoretical foundations of image and video processing, computer vision, data fusion, statistical and spectral modeling,

CyberIntegrator is a highly interactive exploratory scientific process management environment to support earth observatories and to address the many needs of scientific processes. The current implementation of Cyber-Integrator enables users (1) to browse registries of data, software tools and computational resources, (2) to create meta-workflows by example (step by step execution), (3) to re-use and re-purpose meta-workflows, (4) to execute meta-workflows locally or remotely, (5) to incorporate heterogeneous code executors and tools, and link them transparently, (6) to provide recommendations about workflow completion, (7) to search for data, tools and resources in registries, and (8) to support processing of streaming data and large size, out-of-core, data.

GeoLearn is designed to enable rapid processing of large size satellite remote sensing data available in HDF EOS format. It has been tested primarily with MODIS land-surface data products. Use and analysis of these datasets are at the heart of a variety of scientific investigations pertaining to the study of the interaction between land-surface and climate, and prediction of terrestrial hydrologic processes.

Image Provenance To Learn (IP2Learn) is a simulation framework that is designed for understanding preservation and reconstruction archival requirements for a class of decisions based on image inspection. IP2Learn allows users to analyze computational costs of information gathering as a function of information granularity and then assess the potential value of preserved information from decision process reconstructions.

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.