The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 255–260, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-255-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 255–260, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-255-2020

  06 Nov 2020

06 Nov 2020

PAIRS (RE)LOADED: SYSTEM DESIGN & BENCHMARKING FOR SCALABLE GEOSPATIAL APPLICATIONS

C. M. Albrecht, N. Bobroff, B. Elmegreen, M. Freitag, H. F. Hamann, I. Khabibrakhmanov, L. Klein, S. Lu, F. Marianno, J. Schmude, X. Shao, C. Siebenschuh, and R. Zhang C. M. Albrecht et al.
  • IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, 10598, NY, USA

Keywords: big data analytics, ML, AI, distributed geo-spatial data structures, Hadoop, HBase, Spark, GeoMesa, PAIRS Geoscope

Abstract. In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.