A graphical method for identifying and extracting irregular sub-regions of large spatial data sets

Recent advances in space-based imaging of Earth and other planets has produced an unprecedented wealth of collected and stored data. The collected data sets include both low resolution data (several megabytes) and high resolution data (several gigabytes). Selected subregions of the collected data are often used as the primary source of input for finite element or finite difference modeling for climatic, hydrology or other large-scale simulation. Of the collected data sets, the high resolution data set is the most preferred however, the most used data set is the low resolution data sets. This is primarily due to the lack of software tools that support visualizing and extracting geolocated information from high resolution planetary data. We propose several novel graphical methods to allow researchers to more easily visualize, select, and extract irregular high resolution sub regions of larger data sets without necessarily loading into memory or displaying an entire high-resolution data file. We have implemented these visualization and extraction methods with a graphical tool that also preserves the essential geolocation information in the exported files. We call our software tool the Feature Extraction Toolkit (FET).