Spatial Data Lake for Smart Cities: From Design to Implementation

Kafando, Rodrique; Decoupes, Rémy; Sautot, Lucile; Teisseire, Maguelonne

doi:10.5194/agile-giss-1-8-2020

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…With the advent of high resolution cameras and more affordable modes of aerial and space-borne imagery, there has been an acute growth in a wide variety of remotely sensed imagery data (RSID) (Wang et al, 2019). Such high resolution RSID provide a rich source of information for a multitude of geo-spatial applications such as spatial data lakes for smart cities (Kafando et al, 2020). Consequently, increased research efforts are being geared to develop efficient RSID storage and querying systems.…”

Section: Introductionmentioning

confidence: 99%

4sa: Optimizing Space Filling Curve Based Grid Cell Indexing to Scalably Manage Remotely Sensed Images in Key-Value Databases

Hewage

Vo²,

Bertolotto³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 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.

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Section: Introductionmentioning

confidence: 99%