Open Source Parallel Image Analysis and Machine Learning Pipeline

Continuum Analytics sees direct usage applications in any NASA project deriving analytical value from multidimensional climate data arrays and hyper- or multispectral remote sensing data, such as climate reanalysis, landscape change analysis, land cover mapping, or drought or vegetation indices. In Phase I the team provided a number of scientific data loading tools for formats common in NASA remote sensing and climate science missions. Phase I work also created parallel ensemble fitting and prediction methods for a variety of unsupervised and supervised machine learning models. Phase II will provide tools for NASA data formats, additional tools for feature engineering in multidimensional climate data arrays, and advanced options for ensemble fitting and prediction, like hierarchical modeling and vote count ensemble averaging. Phase II will also include work on a web-based map interface and demonstrations of how ELM MLT may be useful in NASA missions like climate reanalysis and land cover classification.

The team sees direct usage application of the image analysis and machine learning pipeline outside of NASA, such as: - NOAA mission-related research to predict changes in climate, weather, oceans and coast, and conserving and managing coasting and marine ecosystems and resources. - DOD/IC - foreign defense and homeland security applications - Commercial infrastructure and engineering, disaster management and mitigation analysis, natural resource monitoring, energy-related exploration and operational management. - Flood and floodplain mapping for insurance adjustments, bridge construction projects, FEMA floodplain definitions, river habitat and restoration projects, and emergency planning at local, state, and federal agencies - Forest disease and insect damage density identification for large commercial forest owners - Snow and ice cover and recession analysis useful in climate change and water management planning at federal, state, and local agencies - Developing spectral identifiers of agricultural crops in healthy versus water and nutrient stressed conditions - Classifying parking lots and roads based on the number of vehicles evidently in the image, an indicator of economic activity and also potentially useful in federal security applications - Mapping ecologically sensitive and geotechnically unstable areas, such as wetlands and mass wasting events, useful for reducing the cost of development review in local, state, and federal environmental agencies, remote asset trackin More »