TY - GEN
T1 - DACMA
T2 - 2022 IEEE International Conference on Big Data, Big Data 2022
AU - Hewage, Chamin Nalinda Lokugam
AU - Laefer, Debra
AU - Bertolotto, Michela
AU - Vo, Anh Vu
AU - Le-Khac, Nhien An
N1 - Funding Information:
ACKNOWLEDGMENTS 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 “UrbanARK: 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:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Aerial images are a special class of remote sensing images, as they are intentionally collected with a high degree of overlap. This high degree of overlap complicates existing index strategies such as R-tree and Space Filling Curve (SFC) based index techniques due to complications in space splitting, granularity of the grid cells and excessive duplication of image object identifiers (IOIs). However, SFC based space ordering can be modified to provide scalable management of overlapping aerial images. This involves overcoming similar IOIs in adjacent grid cells, which would naturally occur in SFC based grids with such data. IOI duplication can be minimized by merging adjacent grid cells through the proposed 'Designing Adjacent Cell Merge Algorithm' (DACMA). This work focuses on establishing a proper adjacent cell merge metric and merge percentage value. Using a highly scalable, distributed HBase cluster for both a single aerial mapping project, and multiple aerial mapping projects, experiments evaluated Jaccard Similarity (JS) and Percentage of Overlap (PO) merge metrics. JS had significant advantages: (i) generating smaller merged regions and (ii) obtaining over 21% and 36% improvement in reducing query response times compared to PO. As a result, JS is proposed for the merge metric for DACMA. For the merge percentage two considerations were dominant: (i) substantial storage reductions with respect to both straight forward SFC-based cell space indexing and 4SA based indexing, and (ii) minimal impact on the query response time. The proposed merge percentage value was selected to optimize the storage (i.e. space) needs and response time (i.e. time) herein named the "Space-Time Trade-off Optimization Percentage"value (or STOP value) is presented.
AB - Aerial images are a special class of remote sensing images, as they are intentionally collected with a high degree of overlap. This high degree of overlap complicates existing index strategies such as R-tree and Space Filling Curve (SFC) based index techniques due to complications in space splitting, granularity of the grid cells and excessive duplication of image object identifiers (IOIs). However, SFC based space ordering can be modified to provide scalable management of overlapping aerial images. This involves overcoming similar IOIs in adjacent grid cells, which would naturally occur in SFC based grids with such data. IOI duplication can be minimized by merging adjacent grid cells through the proposed 'Designing Adjacent Cell Merge Algorithm' (DACMA). This work focuses on establishing a proper adjacent cell merge metric and merge percentage value. Using a highly scalable, distributed HBase cluster for both a single aerial mapping project, and multiple aerial mapping projects, experiments evaluated Jaccard Similarity (JS) and Percentage of Overlap (PO) merge metrics. JS had significant advantages: (i) generating smaller merged regions and (ii) obtaining over 21% and 36% improvement in reducing query response times compared to PO. As a result, JS is proposed for the merge metric for DACMA. For the merge percentage two considerations were dominant: (i) substantial storage reductions with respect to both straight forward SFC-based cell space indexing and 4SA based indexing, and (ii) minimal impact on the query response time. The proposed merge percentage value was selected to optimize the storage (i.e. space) needs and response time (i.e. time) herein named the "Space-Time Trade-off Optimization Percentage"value (or STOP value) is presented.
KW - aerial images
KW - scalability
KW - space filling curves
KW - space-time trade-off
UR - http://www.scopus.com/inward/record.url?scp=85147961435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147961435&partnerID=8YFLogxK
U2 - 10.1109/BigData55660.2022.10020833
DO - 10.1109/BigData55660.2022.10020833
M3 - Conference contribution
AN - SCOPUS:85147961435
T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
SP - 4916
EP - 4925
BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
A2 - Tsumoto, Shusaku
A2 - Ohsawa, Yukio
A2 - Chen, Lei
A2 - Van den Poel, Dirk
A2 - Hu, Xiaohua
A2 - Motomura, Yoichi
A2 - Takagi, Takuya
A2 - Wu, Lingfei
A2 - Xie, Ying
A2 - Abe, Akihiro
A2 - Raghavan, Vijay
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 December 2022 through 20 December 2022
ER -