TY - JOUR
T1 - 4SA
T2 - 7th International Conference on Smart Data and Smart Cities, SDSC 2022
AU - Lokugam Hewage, C. N.
AU - Vo, A. V.
AU - Bertolotto, M.
AU - Le-Khac, N. A.
AU - Laefer, D.
N1 - Funding Information:
This publication originated from research supported in part by a grant from Science Foundation Ireland under Grant number SFI - 17US3450. Further funding for this project was provided by the National Science Foundation as part of the project “Urb-anARK: Assessment, Risk Management, Knowledge for Coastal Flood Risk Management in Urban Areas” NSF Award 1826134, jointly funded with Science Foundation Ireland (SFI - 17US3450) and Northern Ireland Trust (Grant USI 137). The clusters used for the testing were provided by NYU High Performance Computing Center. The aerial image data of Dublin were acquired with funding from the European Research Council [ERC-2012-StG-307836] and additional funding from Science Foundation Ireland [12/ERC/I2534].
Publisher Copyright:
© Author(s) 2022.
PY - 2022/10/14
Y1 - 2022/10/14
N2 - State-of-the-art remote sensing image management systems adopt scalable databases and employ sophisticated indexing techniques to perform window and containment queries. Many rely on space-filling curve (SFC) based index techniques designed for key-value databases and are predominantly employable for images that are iso-oriented. Critically, these indexes do not consider the high degree of overlap among images that exists in many data sets and the affiliated storage requirements. Specifically, employing an SFC-based grid cell index approach in consort with ground footprint coverage of the images requires storage of a unique image object identification (IOI) for each image in every grid cell where overlap occurs. Such an approach adversely affects both storage and query response times. In response, this paper presents an optimization technique for an SFC-based grid cell space indexing. The optimization is specifically designed for window and containment queries where the region of interest overlaps with at least a 2 × 2 grid of cells. The technique is based on four cell removal steps, thus called "four step algorithm"(4SA). Each step employs a unique spatial configuration to check for continuous spatial extent. If present, the IOI of the target cell is omitted from further consideration. Analysis and experiments on real world and synthetic image data demonstrated that 4SA improved storage demands by 41.3% - 47.8%. Furthermore, in the performed querying experiments, only 42% of IOI elements needed to be processed, thus yielding a 58% productivity gain. The reduction of IOI elements in querying also impacted the CPU execution time (3.0% - 5.2%). The 4SA also demonstrated data scalability and concurrent user scalability in querying large regions by completing the index searching and concurrent user scalability 1.86% - 3.35% faster than when 4SA was not applied.
AB - State-of-the-art remote sensing image management systems adopt scalable databases and employ sophisticated indexing techniques to perform window and containment queries. Many rely on space-filling curve (SFC) based index techniques designed for key-value databases and are predominantly employable for images that are iso-oriented. Critically, these indexes do not consider the high degree of overlap among images that exists in many data sets and the affiliated storage requirements. Specifically, employing an SFC-based grid cell index approach in consort with ground footprint coverage of the images requires storage of a unique image object identification (IOI) for each image in every grid cell where overlap occurs. Such an approach adversely affects both storage and query response times. In response, this paper presents an optimization technique for an SFC-based grid cell space indexing. The optimization is specifically designed for window and containment queries where the region of interest overlaps with at least a 2 × 2 grid of cells. The technique is based on four cell removal steps, thus called "four step algorithm"(4SA). Each step employs a unique spatial configuration to check for continuous spatial extent. If present, the IOI of the target cell is omitted from further consideration. Analysis and experiments on real world and synthetic image data demonstrated that 4SA improved storage demands by 41.3% - 47.8%. Furthermore, in the performed querying experiments, only 42% of IOI elements needed to be processed, thus yielding a 58% productivity gain. The reduction of IOI elements in querying also impacted the CPU execution time (3.0% - 5.2%). The 4SA also demonstrated data scalability and concurrent user scalability in querying large regions by completing the index searching and concurrent user scalability 1.86% - 3.35% faster than when 4SA was not applied.
KW - grid indexing
KW - key-value databases
KW - Remotely sensed images
KW - scalability
KW - space filling curves
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U2 - 10.5194/isprs-annals-X-4-W3-2022-143-2022
DO - 10.5194/isprs-annals-X-4-W3-2022-143-2022
M3 - Conference article
AN - SCOPUS:85140332766
SN - 2194-9042
VL - 10
SP - 143
EP - 150
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
IS - 4/W3-2022
Y2 - 19 October 2022 through 21 October 2022
ER -