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3D Spatial Coverage Measurement of Aerial Images

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11961)

Abstract

Unmanned aerial vehicles (UAVs) such as drones are becoming significantly prevalent in both daily life (e.g., event coverage, tourism) and critical situations (e.g., disaster management, military operations), generating an unprecedented number of aerial images and videos. UAVs are usually equipped with various sensors (e.g., GPS, accelerometers and gyroscopes) so provide sufficient spatial metadata that describe the spatial extent (referred to as aerial field-of-view) of recorded imagery. Such spatial metadata can be used efficiently to answer a fundamental question about how well a collection of aerial imagery covers a certain area spatially by evaluating the adequacy of the collected aerial imagery and estimating their sufficiency. This paper provides an answer to such questions by introducing 3D spatial coverage measurement models to collectively quantify the spatial and directional coverage of a geo-tagged aerial image dataset. Through experimental evaluation using real datasets, the paper demonstrates that our proposed models can be implemented with highly efficient computation of 3D space geometry.

Keywords

  • Geo-tagged aerial image
  • 3D spatial coverage
  • Aerial field of view

A. Alfarrarjeh and Z. Ma—These authors contributed equally to this work.

Z. Ma—This author contributed to the work during his research visit at USC.

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Notes

  1. 1.

    In the near future, aerial images can be potentially used for smart city applications, e.g., street cleanliness classification [1] and material recognition [3].

  2. 2.

    Regarding Eq. 2, each combination of signs corresponds to the four different points.

  3. 3.

    Other index structures include R-tree [11], Quad-tree [10], and Grid.

  4. 4.

    The proof is omitted due to space limitation. In practice, it can be lower, but still prohibitive.

  5. 5.

    The proof is omitted due to limited space.

  6. 6.

    m can be further reduced in ECM and WCM, which we will not discuss here.

  7. 7.

    Subsetting does not affect the accuracy of the coverage models.

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Acknowledgment

This work was supported in part by NSF grants IIS-1320149 and CNS-1461963, the USC Integrated Media Systems Center, and unrestricted cash gifts from Oracle and Google.

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Correspondence to Abdullah Alfarrarjeh .

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Alfarrarjeh, A., Ma, Z., Kim, S.H., Shahabi, C. (2020). 3D Spatial Coverage Measurement of Aerial Images. In: , et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_30

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