CyberGIS for Scalable Remote Sensing Data Fusion

Satellite remote sensing data products are widely used in many applications and science domains ranging from agriculture and emergency management to Earth and environmental sciences. Researchers have developed sophisticated and computationally intensive models for processing and analyzing such data with varying spatiotemporal resolutions from multiple sources. However, the computational intensity and expertise in using advanced cyberinfrastructure have held back the scalability and reproducibility of such models. To tackle this challenge, this research employs the CyberGIS-Compute middleware to achieve scalable and reproducible remote sensing data fusion across multiple spatiotemporal resolutions by harnessing advanced cyberinfrastructure. CyberGIS-Compute is a cyberGIS middleware framework for conducting computationally intensive geospatial analytics with advanced cyberinfrastructure resources such as those provisioned by XSEDE. Our case study achieved remote sensing data fusion at high spatial and temporal resolutions based on integrating CyberGIS-Compute with a cutting-edge deep learning model. This integrated approach also demonstrates how to achieve computational reproducibility of scalable remote sensing data fusion.