Skip to main content

3D Spatial Coverage Measurement of Aerial Images

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2020)

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

Included in the following conference series:


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.

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  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.


  1. Alfarrarjeh, A., et al.: Image classification to determine the level of street cleanliness: a case study. In: BigMM, pp. 1–5. IEEE (2018)

    Google Scholar 

  2. Alfarrarjeh, A., et al.: Spatial coverage measurement of geo-tagged visual data: a database approach. In: BigMM, pp. 1–8. IEEE (2018)

    Google Scholar 

  3. Alfarrarjeh, A., et al.: Recognizing material of a covered object: a case study with graffiti. In: ICIP, pp. 2491–2495. IEEE (2019)

    Google Scholar 

  4. Alfarrarjeh, A., et al.: A web-based visualization tool for 3D spatial coverage measurement of aerial images. In: Cheng, W.-H., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 715–721. Springer, Cham (2020)

    Google Scholar 

  5. Alfarrarjeh, A., et al.: Scalable spatial crowdsourcing: a study of distributed algorithms. In: MDM, vol. 1, pp. 134–144. IEEE (2015)

    Google Scholar 

  6. Ay, S.A., et al.: Viewable scene modeling for geospatial video search. In: MM, pp. 309–318. ACM (2008)

    Google Scholar 

  7. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)

    Article  MathSciNet  Google Scholar 

  8. Bringmann, K., Friedrich, T.: Approximating the volume of unions and intersections of high-dimensional geometric objects. CG 43(6), 601–610 (2010)

    MathSciNet  MATH  Google Scholar 

  9. Del B., A., et al.: Visual coverage using autonomous mobile robots for search and rescue applications. In: SSRR, pp. 1–8. IEEE (2013)

    Google Scholar 

  10. Finkel, R.A., Bentley, J.L.: Acta informatica 4(1), 1–9 (1974)

    Article  Google Scholar 

  11. Guttman, A.: R-trees: A dynamic Index Structure for Spatial Searching. In: SIGMOD, pp. 47–57. ACM Press (1984)

    Google Scholar 

  12. Kazemi, L., Shahabi, C.: GeoCrowd: enabling query answering with spatial crowdsourcing. In: SIGSPATIAL GIS, pp. 189–198. ACM (2012)

    Google Scholar 

  13. Kim, S.H., et al.: TVDP: translational visual data platform for smart cities. In: ICDEW, pp. 45–52. IEEE (2019)

    Google Scholar 

  14. Lillian, B.: Xcel energy kicks off drone inspections beyond line of sight (2018).

  15. Lu, Y., Shahabi, C.: Efficient indexing and querying of geo-tagged aerial videos. In: SIGSPATIAL GIS, pp. 1–10. ACM (2017)

    Google Scholar 

  16. Oettershagen, P., Stastny, T., Mantel, T., Melzer, A., Rudin, K., Gohl, P., Agamennoni, G., Alexis, K., Siegwart, R.: Long-endurance sensing and mapping using a hand-launchable solar-powered UAV. In: Wettergreen, D.S., Barfoot, T.D. (eds.) Field and Service Robotics. STAR, vol. 113, pp. 441–454. Springer, Cham (2016).

    Chapter  Google Scholar 

  17. Oettershagen, P., et al.: Design of small hand-launched solar-powered UAVs: from concept study to a multi-day world endurance record flight. J. Field Robot. 34(7), 1352–1377 (2017)

    Article  Google Scholar 

  18. Papatheodorou, S., et al.: Collaborative visual area coverage. Robot. Auton. Syst. 92, 126–138 (2017)

    Article  Google Scholar 

  19. Sameer, A., et al.: Building Rome in a day. In: ICCV, pp. 72–79. IEEE Computer Society (2009)

    Google Scholar 

Download references


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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Abdullah Alfarrarjeh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alfarrarjeh, A., Ma, Z., Kim, S.H., Shahabi, C. (2020). 3D Spatial Coverage Measurement of Aerial Images. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics