INTEGRATED DATA CUBES FOR LARGE-SCALE SPATIOTEMPORAL ANALYSIS OF REMOTE SENSING DATA IN ARCGIS

Advancements in sensor technology have led to significant growth in the volume and variety of geospatial data. Optical, radar, lidar, and sonar sensor technologies have presented us with valuable opportunities to understand our changing world. However, a key challenge still lies in developing data models that can efficiently manage diverse types of spatial and temporal data collected from these sensors to enable information extraction. In this paper, we describe the integrated data cubes within ArcGIS that effectively handle spatial and temporal data. Our approach includes a mosaic dataset for managing satellite time series and gridded climate data, a cloud raster format (CRF) file for storing pixel-aligned analysis-ready data, and a trajectory dataset for managing points from altimetry sensors. We present several use cases to demonstrate the analysis capabilities based on these data models.