A Scalable System for Searching Large-scale Multi-sensor Remote Sensing Image Collections
Yifan Zhao, Yang Xian, Ranga Raju Vatsavai · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Huge amounts of remote sensing data collected from hundreds of operational satellites in conjunction with on-demand UAV based imaging products are offering unprecedented capabilities towards monitoring dynamic earth resources. However, searching for the right combination of imagery products that satisfy an application requirement is a daunting task. Earlier efforts at streamlining remote sensing data discovery include NASA’s Earth Observing System (EOS) Data and Information System (EOSDIS), USGS Global Visualization Viewer (GloVis), and several other research systems like Minnesota MapServer. These systems were built on top of metadata harvesting, indexing, keyword searching modules which were not scalable and interoperable. To address these challenges, recently the SpatioTemporal Asset Catalog (STAC) specification was developed to provide a common language to describe a range of geospatial information, so that data products can be more easily indexed and discovered. In this paper we present an highly scalable STAC API based system with spatiotemporal indexing support. Experimental evaluation shows that our spatiotemporal indexing based queries are 1000x faster than standard STAC API server.