Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

ABSTRACT Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. For disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. Data processing and distribution requires a processing pipeline that can efficiently manage the necessary storage, computing utilities, and data handling so that products are usable. Recent expansion of ready availability cloud computing technology capabilities that can accept a greater multitude of operating systems, application programming interfaces (APIs), and web based processing tools has enabled a powerful new computing infrastructure resource. The utility of cloud computing platforms for automated geoprocessing capabilities (data handling and application development requirement), were investigated in this study. A prototypical set of image manipulation and transformation processes using sample Unmanned Airborne System (UAS) data acquired from NASA Ames Research Center were developed to create value-added products and tested for implementation on the ”cloud.” Initial steps involved creating and testing open source software on a local prototype platform, and then transitioning this code, with associated environment requirements, into an analogous, but memory and processor enhanced cloud platform. A NASA Nebula data processing cloud instance was then used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image index processing functions such as NDVI (Normalized Difference Vegetation Index), and mosaicing. Findings observed on Nebula were evaluated for bulk geoprocessing capabilities based on data handling, application development requirements, and processing speed.