Cloudmaps from static ground-view video

Nathan Jacobs, Scott Workman, Richard Souvenir

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Cloud shadows dramatically affect the appearance of outdoor scenes. We describe three approaches that use video of cloud shadows to estimate a cloudmap, a spatio-temporal function that represents the clouds passing over the scene. Two of the methods make assumptions about the camera and/or scene geometry. The third method uses techniques from manifold learning and does not require such assumptions. None of the methods require directly viewing the clouds, but instead use the pattern of intensity changes caused by the cloud shadows. An accurate estimate of the cloudmap has potential applications in solar power estimation and forecasting, surveillance, and graphics. We present a quantitative evaluation of our methods on synthetic scenes and show qualitative results on real scenes. We also demonstrate the use of a cloudmap for foreground object detection and video editing.

Original languageEnglish
Pages (from-to)154-166
Number of pages13
JournalImage and Vision Computing
StatePublished - Aug 1 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.


  • Clouds
  • Image formation
  • Lighting estimation
  • Scene factorization
  • Solar forecasting
  • Time-lapse

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition


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